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This chapter discusses drug repurposing efforts carried out globally for coronavirus disease 2019 (COVID-19) using in silico methods. The concept of drug repurposing, information required for in silico approaches and its importance in the light of the current pandemic is highlighted along with regulatory considerations. The chapter focuses on other potential targets for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as well as human ones for developing therapeutics for COVID-19. It categorizes in silico methods according to structure-based, ligand-based and other approaches. The structure-based approaches are further categorized based on individual targets, for which a summary of some important research reports is provided. The studies targeting the main protease of SARS-CoV-2 outnumber the research on other viral proteins. Although it is known that modulating human targets involved in COVID-19 could be important and beneficial, not much emphasis has been given to computational research on these targets. The main purpose of the in silico research is to provide clues to experimentalists to select drugs for target-wise screening or propose mechanisms of action for selected drugs. The present review assesses the in silico predictions provided in each report based on available experimental data for inhibition of the specific target(s) or inhibition of SARS-CoV-2 cellular infection. Finally, a discussion is provided on the areas of improvement for in silico approaches.

The coronavirus disease 2019 (COVID-19) pandemic that the world is facing currently is a rare and once-in-a-lifetime situation. The fast spread of the virus worldwise has been alarming, and the number of infected people and deaths has been increasing since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection originated in December 2019 in Wuhan, China. Currently, significant efforts are being made to find treatments as well as restrict its spread. In an emergency like this, the usual entire drug discovery effort for finding a new drug is not feasible (it takes approximately 10–12 years to identify a new drug and get it approved by the regulatory authorities). In view of this, scientific efforts have been focused on a drug-repurposing approach to identifying treatment for COVID-19. Both antiviral drugs that have been approved for the treatment of SARS-CoV-2 infection (remdesivir and favipiravir) have emerged from drug repurposing.

Drug repurposing is investigating the use of an existing drug to treat an entirely different disease, and is carried out using two pathways:

  1. through known target(s) of the drug where new information shows that this target is also involved in new disease etiology;

  2. through identification of new target(s) for the drug by which it alleviates the new disease.

In the case of COVID-19, both approaches are valid. Some drugs have been proposed to work through known targets of SARS-CoV-2 (e.g. remdesivir is an inhibitor of RNA-dependent RNA polymerase), while a few drugs have been identified to work through entirely new targets (e.g. dipyridamole as an inhibitor of the main protease of SARS-CoV-2).

Repurposing opportunities exist because drugs modulate multiple biological targets, designated as on- and off-targets, that are involved in multiple biological processes. Based on drug promiscuity data it is believed that every drug has a potential to bind to approximately 6–7.5 targets.1  As drug discovery pipelines are focused on a disease of interest, the therapeutic application in other areas is likely to be missed. These drugs can either be approved and marketed compounds used daily in clinical settings, or drugs that have been shelved (where molecules failed in clinical trials or for which projects have been discontinued for various reasons). Thus, drug repurposing can be defined as expanding the scope of approved drugs and reviving the shelved ones.

As the safety, tolerability, pharmacokinetics, pharmacodynamics and clinical information in humans is known for repurposed drugs, the existing dose and formulation, if suitable, can be directly investigated in clinical study for the treatment of a new disease. If a higher dose or frequency of dosing is required, it can be investigated in clinical study if a known therapeutic window allows it; otherwise more safety data are required. Similarly, if a new route of administration is required, relevant bridging toxicity information suitable for the new route of administration should be generated and all phases of clinical studies are required.

Dr Christopher Austin (Director, National Center for Advancing Translational Sciences at the United States National Institutes of Health) has remarked that “Drug repurposing seems tantalizingly simple. Conservatively, there are 6500 human diseases that have no regulatory-approved treatments whatsoever. At the current rate of progress, it will be 2000 years before every human disease is treatable. What percentage of those 6500 currently untreatable diseases is ameliorable, to some degree, by a drug you can get at [your local pharmacy]? Shame on us if we can't figure out a way to make these available to patients suffering from disabling and lethal diseases. This is an eminently solvable problem.”2 

The world has already seen the success of drug repurposing in terms of remdesivir, a drug for the Ebola virus which has been approved and used for the treatment of SARS-CoV-2 infection.

The regulatory pathway to develop a repurposed drug is less elaborate, since existing safety, pharmacokinetics and toxicity information generated for original indication can be used, and only the required bridging studies need be performed to establish safety of the drug in new disease. However, clinical trials are required to establish efficacy in a new disease before filing a new drug application. The United States Food and Drug Administration (FDA) offers 3 years of data exclusivity for a new use of a previously marketed drug, independent of applicable patents.

With a new infection such as SARS-CoV-2, finding a repurposed drug is the most viable option, since new discovery will take significant time, and by the time a drug would be approved for treatment, substantial human loss would already have taken place, and the disease may not remain relevant. In addition, repurposing has enormous advantages over novel drugs in terms of faster commencement of clinical trials, as the drug may be available off the shelf. If found effective in well-designed clinical studies, the repurposed drug would benefit from:

  1. available bulk active pharmaceutical ingredient and formulation manufacturing processes and immediate accessibility to patients;

  2. lower cost compared to new drug discovery, as safety data for the drug candidate are available, and only efficacy in the new disease is to be established;

  3. shorter time to market;

  4. existing awareness among physicians about the drug, its side-effects, contraindications and drug–drug interactions, based on which it can be prescribed, keeping in view the pre-existing conditions of the patient.

At present there are approximately 269 repurposed drugs in clinical investigation for COVID-19, many of which have been identified using in silico methods. The present review analyses and reports methods that are useful in exploring such repurposing opportunities.

In general, several approaches for drug repurposing, including in silico methods, have been used by various researchers. Some of the experiment-based drug repurposing approaches are mentioned here. However, these are only representative and by no means exhaustive.

Genetic expression profiling has been used by several groups in the context of drug repurposing. The mRNA expression data from patients with various diseases is compared with similar data on mRNA expressed in vitro by a set of drugs. The expression data are then analysed in terms of signatures and drugs that have the opposite signature to the disease are identified. This approach showed that the histamine H2 antagonist cimetidine would be effective in lung cancer, and the antiepileptic topiramate would be effective in inflammatory bowel disease.3  Although this approach may be useful in cases of acute respiratory distress syndrome (ARDS) in COVID-19, it has not been reported yet.

An in vitro screen is routinely utilized for drug repurposing.4  In one study, a primary organotypic spinal cord slice culture was used to screen FDA-approved compounds. The results identified interesting and previously unknown effects for ceftriaxone and harmine as upregulators of the glutamate transporter GLT-1. GLT-1 in glia had previously been reported to be associated with the development of amyotrophic lateral sclerosis.5 

Cell-based high-throughput screening of 4910 drug-like compounds in four prostate cancer and two non-malignant prostate cell lines identified disulfiram, a drug used for alcohol abuse, as a selective antineoplastic agent.6  A zebrafish model used to evaluate medications for tobacco dependence found that apomorphine and topiramate modified nicotine-induced and ethanol-induced behavior.7 

This standard approach has been used for screening drugs against SARS-CoV-2 infection and some of its targets8–10  (also see ref. 173).

This approach identifies compounds that show disease-relevant effects in model systems without prior knowledge of the target(s) affected. A discovery platform for screening known drug-like compounds using a battery of in vivo assays spanning a broad range of therapeutic areas have been explored.11  This approach could identify new indication for the failed drug MLR1023 from Pfizer as a potential new antidiabetic agent via insulin sensitization.12 

Proteomic techniques such as affinity chromatography and mass spectrometry have been used to identify binding partners for an increasing number of drugs to identify their targets for drug repurposing opportunities.13 

This has been used to predict thermal stabilization of target proteins by drug-like ligands that possess the appropriate cellular affinity.14 In vitro competition binding assay has been used to evaluate 38 kinase inhibitors against a panel of 317 distinct human protein kinases which exhaustively identified a total of 3175 binding interactions. Sorafenib and dasatinib showed higher affinity to secondary kinase targets than their known primary target, potentially limiting their use in patient populations.15 

Among several experimental approaches, only phenotype screening and in vitro screening against the virus have been used for drug repurposing for COVID-19, since reliable target-based assays for high-throughput screening were not available initially for SARS-CoV-2. Furthermore, exhaustive screening of all drugs with all targets associated with COVID-19 is an enormous task requiring huge resources. Using in silico methods, such exhaustive screening is indeed feasible, but has rarely been reported by research groups. We have carried out one such effort using in-house reverse virtual high-throughput screening technology on 21 viral and human targets. This has yielded a shortlist of 42 candidates, of which four were tested for SARS-CoV-2 inhibition and found to be active; one of them is undergoing a phase 3 clinical trial in India.

The discussion hereafter will focus on in silico efforts for drug repurposing reported on SARS-CoV-2 and human targets involved in COVID-19. The in silico approaches have been categorized as structure-based, ligand-based and other approaches. Details of the methods of these approaches and how they have been used for drug repurposing have been compiled recently.16  The present review summarizes some major in silico drug repurposing efforts reported in the literature at 31 August 2020. The review only covers different in silico approaches used for repurposing of already approved drugs. The screening of databases other than those of approved drugs, such as ZINC, phytochemicals, polyphenols, dietary supplements or any other compound collection is not covered in this review. Furthermore, comments are made by the authors on the utility and success of reported computational efforts based on existing experimental results. The consensus screening methodology emerging from various research articles is presented and future directions are also discussed.

Coronaviruses are a family of single-stranded RNA viruses with a relatively large size. The viral membrane encapsulating the RNA is studded with spike glycoprotein which gives the coronaviruses their crown-like appearance (Figure 1.1).

Figure 1.1

Structure of SARS-CoV-2.

Figure 1.1

Structure of SARS-CoV-2.

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SARS-CoV-2 is a member of the coronavirus family containing a 29.9 kb RNA messenger, encoding 13 open reading frames, as shown in Figure 1.2.

Figure 1.2

Genomic structure of SARS-CoV-2.

Figure 1.2

Genomic structure of SARS-CoV-2.

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This genome encodes several structural and nonstructural proteins, with 28 proteins in all (https://viralzone.expasy.org/9076). Structural proteins are important for virion assembly as well as for causing infection. The four different types of structural proteins are N (nucleocapsid), S (spike), E (envelope) and M (membrane). The N-protein (419 aa) encapsidates the viral genome, while the other structural proteins S, E and M comprise the surrounding lipid bilayer envelope. The M- (222 aa) and E- (75 aa) proteins are required for virus morphogenesis, assembly and budding, whereas the S-glycoprotein (1273 aa) is a fusion viral protein comprising two subunits (S1 and S2). The S1 subunit comprises a signal peptide, N-terminal domain (NTD) and receptor-binding domain (RBD) of 253 aa, and the S2 unit comprises 608 aa. The orf1ab gene constitutes the major portion (two-thirds) of the genome, and encodes for 16 non-structural proteins (NSPs): NSP1 to NSP16. Furthermore, there are eight accessory proteins in the genome of SARS-CoV-2: Orf3a, Orf6, Orf7a, Orf7b, Orf8, Orf9b, Orf10 and Orf14.

The SARS-CoV-2 spike glycoprotein (S-protein) mediates viral entry into the host cells by binding to surface angiotensin-converting enzyme (ACE)-2 receptors. The S1 and S2 subunits of the S-protein are responsible for SARS-CoV-2 binding to host cell surface receptors and mediating virus–cell and cell–cell membrane fusion, respectively. The entry of SARS-CoV-2 into cells is dependent on cell–cell fusion mediated by cleavage of the S-protein by the host protease furin at the S1/S2 site. The activation of S-protein by TMPRSS2 in human lung cells is dependent on the furin-mediated pre-cleavage of the S-protein at the S1/S2 site.17  Along with the spike structural integrity, this cleavage activation by furin, cathepsin L, neuropilin-1 and TMPRSS2 plays a key role in the invasion and virulence of SARS-CoV-2. Spike protein is a vital component of the virus, as it also plays a key role in neutralizing host antibodies and the T-cell response, thus compromising the host's immunity.18 

The S1 domain is marked as a major antigen on the surface of the coronavirus and comprises the NTD and the C-terminal domain. The head region of S1 comprises the RBD, which is responsible for recognizing the host cell receptor (ACE-2). The membrane fusion between the viral particles and the host receptor is initiated by interactions between key residues of the RDB and ACE-2. Interacting residues between viral RBD and host ACE-2 receptors are identified to be LEU455, PHE486, GLN493, SER494 and ASN501.19  Another report identified LYS455, PHE456, ALA475, PHE486, PHE490 and GLN493 residues to be involved in the spike RBD and ACE-2 binding.20 

The nucleocapsid (N)-protein packages the positive-strand viral genome RNA into a helical ribo-nucleocapsid and plays a fundamental role during virion assembly through its interactions with the viral genome and membrane (M)-protein. N-protein is crucial in enhancing the efficiency of sub-genomic viral RNA transcription and viral replication. It regulates cellular processes such as cytoskeleton reorganization or host cell apoptosis and the cell cycle. In addition, N-protein can elicit the protective immune response of the host, due to highly immunogenic phosphoprotein.

N-protein is used as a diagnostic tool, due to its conserved amino acid sequence and strong immunogenicity. Quantification of virus in a test sample can be done by quantitative real-time reverse transcriptase polymerase chain reaction (qRT-PCR) followed by immunofluorescence microscopy to determine N-protein expression 48 h post-infection. The N terminal RNA-binding domain (NTD), SER/ARG (SR)-rich central linker region and C-terminal dimerization domain (CTD) are the three domains of N-protein. The central linker is the chief phosphorylation site, while the NTD and CTD are involved in executing the binding and oligomerization of RNA.21  A reported structural analysis shows that the NTD rich in basic amino acids is a fist-shaped structure, while flexibility is provided by the loops of β-sheets in the core region.21 

The envelope (E)-protein is the smallest of the SARS-CoV-2 structural proteins, with a 8.4–12 kDa size. E-protein comprises of two distinct domains; the charged cytoplasmic tail, and the hydrophobic domain. It is an integral membrane protein embedded in the envelope, but also localized in the endoplasmic reticulum (ER), golgi and ER–golgi apparatus intermediate compartment (ERGIC), once a host cell has been infected. The pentamer formed functions as an ion channel, hence the name E-channel or viroporin17 .

The viral morphogenesis requires E-protein to play a special role, especially during viral assembly and viral progress. The E-protein also assists in increasing viral titer and mature progenies. Studies have shown that by co-ordinating with other intracellular proteins, the E-protein modulates their activity. The SARS-CoV-2 E-protein can also act as a virulence factor.

The membrane (M)-protein provides the framework for the virion particle. The M-protein spans an envelope thrice, comprising a long C terminal and short N-terminal. M-protein, along with other structural proteins, forms a special assembly. M-protein is also involved in aiding protein–protein interaction and maintaining viral intracellular homeostasis. This protein is glycosylated in the Golgi apparatus. This glycosylation of the M-protein is important for the virion to fuse into the cell and to make protein antigenic. The M-protein plays a key role in stimulating virions in the cell. The virus N-protein also forms a complex by binding to genomic RNA and the M-protein elicits the production and formation of interacting virions in the ERGIC with this complex.

The non-structural and accessory proteins play vital roles in several functions such as viral transcription, replication, etc. described in Table 1.1.

Table 1.1

Function of non-structural and accessory proteins of SARS-CoV-2a

Protein (AA)Other namesFunction
NSP1 (180)  Induces host mRNA (leader protein) cleavage, induces specifically host mRNA degradation and inhibits IFN-1 production 
NSP2 (638)  Binds to host proteins prohibitin 1 and 2 
NSP3a (1945) Papain-like proteinase Releases NSPs 1, 2 and 3 and antagonizes the host's innate immunity 
NSP4 (500)  Membrane rearrangement 
NSP5a (306) 3C-like proteinase (3CLpro) or main protease (Mpro) Cleaves at 11 sites of NSP polyprotein, directly mediates the maturation of NSPs 
NSP6 (290)  Generates autophagosomes 
NSP7 (83)  Dimerizes with NSP8, co-factor of NSP12 
NSP8 (198)  Stimulates NSP12 
NSP9 (113)  Binds to helicase, RNA binding protein 
NSP10 (139)  Co-factor for activation of NSP14 and NSP16 replicative enzymes 
NSP11 (13)  Unknown 
NSP12a (932) RNA-dependent RNA polymerase (RdRp) Copies viral RNA from an RNA template 
NSP13 (601) Helicase Unwinds duplex RNA, 5′-capping of RNA, antagonizez IFN-1 
NSP14 (527)  5′-capping of RNA (3′ to 5′ exonuclease, guanine N7-methyltransferase), antagonizes IFN-1 
NSP15 (346) EndoRNAse/endoribonuclease Degrades RNA to evade host defense, antagonizes IFN-1 
NSP16 (298) 2′-O-ribose-methyltransferase 5′-cap RNA, methylation (adenine) 
Orf3a (275)  Activates the NLRP3 inflammasome, virus replication, pathogenesis 
Orf6 (61)  Blocks the expression of STAT1-activated genes inhibiting antiviral activity 
Orf7a (121)  Involved in blockage of cell cycle progression at G0/G1 phase 
Orf7b (43)  Essential for Golgi compartment localization 
Orf8 (121)  Disrupts IFN-1 signaling 
Orf9b (97)  Inhibits IFN-1 production 
Orf10 (38)  Encodes a functional protein 
Orf14 (73)  May play a role in host–virus interaction. 
Protein (AA)Other namesFunction
NSP1 (180)  Induces host mRNA (leader protein) cleavage, induces specifically host mRNA degradation and inhibits IFN-1 production 
NSP2 (638)  Binds to host proteins prohibitin 1 and 2 
NSP3a (1945) Papain-like proteinase Releases NSPs 1, 2 and 3 and antagonizes the host's innate immunity 
NSP4 (500)  Membrane rearrangement 
NSP5a (306) 3C-like proteinase (3CLpro) or main protease (Mpro) Cleaves at 11 sites of NSP polyprotein, directly mediates the maturation of NSPs 
NSP6 (290)  Generates autophagosomes 
NSP7 (83)  Dimerizes with NSP8, co-factor of NSP12 
NSP8 (198)  Stimulates NSP12 
NSP9 (113)  Binds to helicase, RNA binding protein 
NSP10 (139)  Co-factor for activation of NSP14 and NSP16 replicative enzymes 
NSP11 (13)  Unknown 
NSP12a (932) RNA-dependent RNA polymerase (RdRp) Copies viral RNA from an RNA template 
NSP13 (601) Helicase Unwinds duplex RNA, 5′-capping of RNA, antagonizez IFN-1 
NSP14 (527)  5′-capping of RNA (3′ to 5′ exonuclease, guanine N7-methyltransferase), antagonizes IFN-1 
NSP15 (346) EndoRNAse/endoribonuclease Degrades RNA to evade host defense, antagonizes IFN-1 
NSP16 (298) 2′-O-ribose-methyltransferase 5′-cap RNA, methylation (adenine) 
Orf3a (275)  Activates the NLRP3 inflammasome, virus replication, pathogenesis 
Orf6 (61)  Blocks the expression of STAT1-activated genes inhibiting antiviral activity 
Orf7a (121)  Involved in blockage of cell cycle progression at G0/G1 phase 
Orf7b (43)  Essential for Golgi compartment localization 
Orf8 (121)  Disrupts IFN-1 signaling 
Orf9b (97)  Inhibits IFN-1 production 
Orf10 (38)  Encodes a functional protein 
Orf14 (73)  May play a role in host–virus interaction. 
a

AA: number of amino acid residues in the protein; IFN: interferon.

The structural and non-structural as well as accessory viral proteins are considered as drug targets, and in silico effort on these largely depends on available information in terms of three-dimensional (3D) structures of these proteins and drug/ligand binding to these targets.

The 3D structure of several SARS-CoV-2 proteins have been solved and are available in the Protein Data Bank (PDB) from www.rcsb.org using X-ray, nuclear magnetic resonance (NMR) imaging or electron microscopy. Currently there are 380 structures reported in the PDB, and Table 1.2 provides structural information of viral targets.

Table 1.2

Protein structures of SARS-CoV-2 available in the PDB

Macromolecule nameNumber of structures in PDBResolution rangeNumber of ligands
Spike glycoprotein (S-protein) 75 1.5 to 6.8 Å 
Nucleocapsid protein (N-protein) 10 1.361 to 2.7 Å 
NSP1 11 2.6 to 3.2 Å 
NSP3a/papain-like proteinase (PLpro) 24 0.95 to 3.18 Å 
NSP5/Main protease (Mpro) 172 1.25 to 2.35 Å 151 
NSP7 15 1.5 to 3.7 Å 
NSP8 15 1.5 to 3.7 Å 
NSP9 2.0 to 2.95 Å 
NSP10 13 1.8 to 2.55 Å 
NSP12a/RNA-directed RNA polymerase (RdRp) 10 2.5 to 3.7 Å 
NSP13 1.94 to 3.5 Å 
NSP15/uridylate-specific endoribonuclease 1.82 to 2.35 Å 
NSP16/2′-O-methyltransferase 20 1.8 to 2.4 Å 16 
ORF3a 2.9 Å 
ORF7a 2.9 Å 
ORF8 2.04 Å 
ORF9b 1.95 Å 
Macromolecule nameNumber of structures in PDBResolution rangeNumber of ligands
Spike glycoprotein (S-protein) 75 1.5 to 6.8 Å 
Nucleocapsid protein (N-protein) 10 1.361 to 2.7 Å 
NSP1 11 2.6 to 3.2 Å 
NSP3a/papain-like proteinase (PLpro) 24 0.95 to 3.18 Å 
NSP5/Main protease (Mpro) 172 1.25 to 2.35 Å 151 
NSP7 15 1.5 to 3.7 Å 
NSP8 15 1.5 to 3.7 Å 
NSP9 2.0 to 2.95 Å 
NSP10 13 1.8 to 2.55 Å 
NSP12a/RNA-directed RNA polymerase (RdRp) 10 2.5 to 3.7 Å 
NSP13 1.94 to 3.5 Å 
NSP15/uridylate-specific endoribonuclease 1.82 to 2.35 Å 
NSP16/2′-O-methyltransferase 20 1.8 to 2.4 Å 16 
ORF3a 2.9 Å 
ORF7a 2.9 Å 
ORF8 2.04 Å 
ORF9b 1.95 Å 

The target-wise structure data currently available in the PDB are provided in Table 1.3.

Table 1.3

Details of protein structures of SARS-CoV-2 available in the PDBa

PDBResolution (Å)Target NameLigand IDCavityPDB IDResolution (Å)Target NameLigand IDCavity
6WQ3 2.10 NSP16 SAH SAM binding site 6ZME 3.00 NSP1   
6WRZ 2.25 NSP16 SAH 6ZLW 2.60 NSP1   
6WVN 2.00 NSP16 SAM 6ZM7 2.70 NSP1   
6XKM 2.25 NSP16 SAM 6ZMT 3.00 NSP1   
6W61 2.00 NSP16 SAM 6ZN5 3.20 NSP1   
6WJT 2.00 NSP16 SAH 6ZMI 2.60 NSP1   
6WKS 1.80 NSP16 SAM 6ZMO 3.10 NSP1   
6W4H 1.80 NSP16 SAM 6ZOJ 2.80 NSP1   
6W75 1.95 NSP16 SAM 6ZP4 2.90 NSP1   
6WKQ 1.98 NSP16 SFG 6ZOK 2.80 NSP1   
6YZ1 2.40 NSP16 SFG 6ZON 3.00 NSP1   
7BQ7 2.37 NSP16 SAM 6WQ3 2.10 NSP10   
5REG 1.67 Mpro LWA Allosteric cavity 1 6WRZ 2.25 NSP10   
5RF8 1.44 Mpro SFY 6WVN 2.00 NSP10   
5RE7 1.79 Mpro T0S Allosteric cavity 2 6XKM 2.25 NSP10   
5RE8 1.81 Mpro T0V 6W4H 1.80 NSP10   
5RF4 1.61 Mpro T5Y 6W61 2.00 NSP10   
5RF9 1.43 Mpro S7D 6W75 1.95 NSP10   
5RFD 1.41 Mpro T6J 6WJT 2.00 NSP10   
5RGJ 1.34 Mpro U0S 6WKS 1.80 NSP10   
6XB1 1.8 Mpro NEN Allosteric cavity 3 6WKQ 1.98 NSP10   
6XB2 2.1 Mpro NEN 6YZ1 2.40 NSP10   
5REC 1.73 Mpro T1J Allosteric cavity 4 6ZCT 2.55 NSP10   
5REE 1.77 Mpro T1M 7BQ7 2.37 NSP10   
5RGS 1.72 Mpro S7V 6WQD 1.95 NSP7   
5RE5 2.07 Mpro T0J Allosteric cavity 5 6WTC 1.85 NSP7   
5RE6 1.87 Mpro O0S 6XEZ 3.50 NSP7   
5REA 1.63 Mpro JGP Allosteric cavity 6 6XIP 1.50 NSP7   
5RFB 1.48 Mpro K3S 6XQB 3.40 NSP7   
5RFC 1.4 Mpro K1Y 6WIQ 2.85 NSP7   
5RGG 2.26 Mpro NZD 7C2K 2.93 NSP7   
6YVF 1.6 Mpro A82 7BTF 2.95 NSP7   
6WNP 1.44 Mpro U5G Active site 7BV1 2.80 NSP7   
5R7Y 1.65 Mpro JFM  7BV2 2.50 NSP7   
5R7Z 1.59 Mpro HWH  7BW4 3.70 NSP7   
5R80 1.93 Mpro RZG  7BZF 3.26 NSP7   
5R81 1.95 Mpro RZJ  6YYT 2.90 NSP7   
5R82 1.31 Mpro RZS  6M5I 2.49 NSP7   
5R83 1.58 Mpro K0G  6M71 2.90 NSP7   
5R84 1.83 Mpro GWS  6WQD 1.95 NSP8 (C-terminal)   
5RE4 1.88 Mpro SZY  6WTC 1.85 NSP8 (C-terminal)   
5RE9 1.72 Mpro LPZ  6XEZ 3.50 NSP8   
5REB 1.68 Mpro T0Y  6XIP 1.50 NSP8 (C-terminal)   
5REF 1.61 Mpro 6SU  6XQB 3.40 NSP8   
5REH 1.8 Mpro AWP  6WIQ 2.85 NSP8 (C-terminal)   
5REJ 1.72 Mpro T1V  7C2K 2.93 NSP8   
5REK 1.74 Mpro T1Y  7BTF 2.95 NSP8   
5REL 1.62 Mpro T2G  7BV1 2.80 NSP8   
5REM 1.96 Mpro T2J  7BV2 2.50 NSP8   
5REN 2.15 Mpro T2V  7BW4 3.70 NSP8   
5REO 1.88 Mpro T2Y  7BZF 3.26 NSP8   
5REP 1.81 Mpro T3G  6YYT 2.90 NSP8   
5RER 1.88 Mpro T3J  6M5I 2.49 NSP8   
5RES 1.65 Mpro T3V  6M71 2.90 NSP8   
5RET 1.68 Mpro T47  6WXD 2.00 NSP9   
5REU 1.69 Mpro T4D  6W4B 2.95 NSP9   
5REV 1.6 Mpro T4J  6WC1 2.40 NSP9   
5REW 1.55 Mpro T4M  6M3M 2.70 Nucleocapsid (N-terminal)   
5REX 2.07 Mpro T4V  6VYO 1.70 Nucleocapsid (N-terminal)   
5REY 1.96 Mpro T4Y  6WJI 2.05 Nucleocapsid (C-terminal)   
5REZ 1.79 Mpro T54  6WKP 2.67 Nucleocapsid (N-terminal)   
5RF0 1.65 Mpro T5D  6WZO 1.42 Nucleocapsid (N-terminal)   
5RF1 1.73 Mpro T5G  6WZQ 1.45 Nucleocapsid (N-terminal)   
5RF2 1.53 Mpro HVB  6YUN 1.44 Nucleocapsid (C-terminal)   
5RF3 1.5 Mpro T5V  6ZCO 1.36 Nucleocapsid (C-terminal)   
5RF6 1.45 Mpro NTG  7C22 2.00 Nucleocapsid (C-terminal)   
5RF7 1.54 Mpro T67  6XDC 2.90 ORF3a   
5RFA 1.52 Mpro JGY  6W37 2.90 ORF7a   
5RFE 1.46 Mpro JGG  6Z4U 1.95 ORF9b   
5RFF 1.78 Mpro T6M  6WOJ 2.20 NSP3 (ADRP) APR ADP-ribose binding site 
5RFG 2.32 Mpro T6V  6W02 1.50 NSP3 (ADRP) APR 
5RFH 1.58 Mpro T6Y  6W6Y 1.45 NSP3 (ADRP) AMP 
5RFI 1.69 Mpro T71  6WCF 1.06 NSP3 (ADRP)   
5RFJ 1.8 Mpro T7A  6WEN 1.35 NSP3 (ADRP)   
5RFK 1.75 Mpro T7D  6WEY 0.95 NSP3 (ADRP)   
5RFL 1.64 Mpro T7G  6YWK 2.20 NSP3 (ADRP)   
5RFM 2.06 Mpro T7J  6YWM 2.16 NSP3 (ADRP)   
5RFN 1.8 Mpro T7P  6VXS 2.03 NSP3 (ADRP)   
5RFO 1.83 Mpro T7S  6YWL 2.50 NSP3 (ADRP)   
5RFP 2.03 Mpro T7V  6WZU 1.79 NSP3 (PLpro)   
5RFQ 1.76 Mpro T7Y  6WRH 1.60 NSP3 (PLpro)   
5RFR 1.71 Mpro T81  6XA9 2.90 NSP3 (PLpro)   
5RFS 1.7 Mpro T84  6XAA 2.70 NSP3 (PLpro)   
5RFT 1.58 Mpro T8A  6XG3 2.48 NSP3 (PLpro)   
5RFU 1.53 Mpro T8D  6YVA 3.18 NSP3 (PLpro)   
5RFV 1.48 Mpro T8J  6W9C 2.70 NSP3 (PLpro)   
5RFW 1.43 Mpro T8M  6WUU 2.79 NSP3 (PLpro) VIR250 Catalytic site 
5RFX 1.55 Mpro T8P  6WX4 1.65 NSP3 (PLpro) VIR251 
5RFY 1.9 Mpro T8S  7JIR 2.09 NSP3 (PLpro) TTT PLpro active site 
5RFZ 1.68 Mpro T8V  7JIT 1.95 NSP3 (PLpro) Y95 
5RG0 1.72 Mpro T8Y  7JIV 2.05 NSP3 (PLpro) VBY 
5RG1 1.65 Mpro T9J  7JIW 2.30 NSP3 (PLpro) VBY 
5RG2 1.63 Mpro T9M  7JN2 1.93 NSP3 (PLpro) Y41 
5RG3 1.58 Mpro T9P  6M71 2.90 RdRp   
5RGH 1.7 Mpro U0M  6XEZ 3.50 RdRp   
5RGI 1.57 Mpro U0P  6XQB 3.40 RdRp   
5RGK 1.43 Mpro U0V  6YYT 2.90 RdRp   
5RGL 1.76 Mpro U0Y  7BTF 2.95 RdRp   
5RGM 2.04 Mpro U1D  7BV1 2.80 RdRp   
5RGN 1.86 Mpro U1A  7BV2 2.50 RdRp F86 Catalytic active site 
5RGO 1.74 Mpro U1G  7BW4 3.70 RdRp   
5RGP 2.07 Mpro U1M  7BZF 3.26 RdRp   
5RGQ 2.15 Mpro U1V  7C2K 2.93 RdRp   
5RGT 2.22 Mpro UHS  6LVN 2.47 Spike (HR domain)   
5RGU 2.1 Mpro UGD  6LXT 2.90 Spike (S2)   
5RGV 1.82 Mpro UGG  6LZG 2.50 Spike (RBD)   
5RGW 1.43 Mpro UGM  6M0J 2.45 Spike (RBD)   
5RGX 1.69 Mpro UGP  6M17 2.90 Spike (RBD)   
5RGY 1.97 Mpro UGS  6M1V 1.50 Spike (S2)   
5RGZ 1.52 Mpro UH1  6VSB 3.46 Spike   
5RH0 1.91 Mpro UH4  6VW1 2.68 Spike (RBD)   
5RH1 1.96 Mpro UGV  6VXX 2.80 Spike   
5RH2 1.82 Mpro UH7  6VYB 3.20 Spike   
5RH3 1.69 Mpro UHA  6W41 3.08 Spike (S1)   
5RH5 1.72 Mpro UHV  6WPS 3.10 Spike   
5RH6 1.6 Mpro UHY  6WPT 3.70 Spike   
5RH7 1.71 Mpro UJ1  6X29 2.70 Spike   
5RH8 1.81 Mpro UHM  6X2A 3.30 Spike   
5RH9 1.91 Mpro UJ4  6X2B 3.60 Spike   
5RHA 1.51 Mpro T8M  6X2C 3.20 Spike   
5RHB 1.43 Mpro USD  6X6P 3.22 Spike   
5RHC 1.58 Mpro USA  6X79 2.90 Spike   
5RHD 1.57 Mpro US7  6XC2 3.11 Spike (S1)   
5RHE 1.56 Mpro UPD  6XC3 2.69 Spike (S1)   
5RHF 1.76 Mpro UPJ  6XC4 2.34 Spike (S1)   
6LU7 2.16 Mpro N3  6XC7 2.88 Spike (S1)   
6LZE 1.5 Mpro FHR  6XCM 3.42 Spike   
6M0K 1.5 Mpro FJC  6XCN 3.66 Spike   
6M2N 2.19 Mpro 3WL  6XDG 3.90 Spike (RBD)   
6W63 2.1 Mpro X77  6XE1 2.75 Spike (S1)   
6W9Q 2.05 Mpro PO4  6XEY 3.25 Spike   
6WTJ 1.9 Mpro K36  6XKL 3.21 Spike   
6WTK Mpro UED  6XLU 2.40 Spike   
6WTT 2.15 Mpro K36  6XM0 2.70 Spike   
6WTT 2.15 Mpro K36  6XM3 2.90 Spike   
6XCH 2.2 Mpro Leupeptin  6XM4 2.90 Spike   
6XHM 1.4 Mpro V2M  6XM5 3.10 Spike   
6XMK 1.7 Mpro QYS  6XR8 2.90 Spike   
6XOA 2.1 Mpro EDO  6XRA 3.00 Spike   
6XQS 1.9 Mpro SV6  6XS6 3.70 Spike   
6XQT 2.3 Mpro NNA  6YLA 2.42 Spike (RBD)   
6XQU 2.2 Mpro U5G  6YM0 4.36 Spike (RBD)   
6XR3 1.45 Mpro V7G  6YOR 3.30 Spike (S1)   
6Y2F 1.95 Mpro O6K  6YZ5 1.80 Spike (RBD)   
6Y2G 2.2 Mpro O6K  6YZ7 3.30 Spike (RBD)   
6YNQ 1.8 Mpro P6N  6Z2M 2.71 Spike (RBD)   
6YT8 2.05 Mpro PK8  6Z43 3.30 Spike   
6YZ6 1.7 Mpro Leupeptin  6Z97 3.40 Spike   
6ZRT 2.1 Mpro SV6  6ZBP 1.85 Spike (RBD)   
6ZRU 2.1 Mpro U5G  6ZCZ 2.65 Spike (RBD)   
7BQY 1.7 Mpro N3  6ZDG 4.70 Spike (ectodomain)   
7BRP 1.8 Mpro HU5  6ZDH 3.70 Spike   
7BRR 1.4 Mpro K36  6ZER 3.80 Spike (RBD)   
7BUY 1.6 Mpro JRY  6ZFO 4.40 Spike (ectodomain)   
7C7P 1.74 Mpro SV6  6ZGE 2.60 Spike   
7C8R 2.3 Mpro TG3  6ZGG 3.80 Spike   
7C8T 2.05 Mpro NOL  6ZGH 6.80 Spike   
7C8U 2.35 Mpro K36  6ZGI 2.90 Spike   
7COM 2.25 Mpro U5G  6ZHD 3.70 Spike   
5RED 1.47 Mpro JJG Allosteric cavity 8 6ZOW 3.00 Spike   
5REI 1.82 Mpro T1S 6ZOX 3.00 Spike   
5RF5 1.74 Mpro HV2 6ZOY 3.10 Spike   
5RGR 1.41 Mpro K1G 6ZOZ 3.50 Spike   
5RH4 1.34 Mpro UHG 6ZP0 3.00 Spike   
6WQF 2.3 Mpro   6ZP1 3.30 Spike   
6WTM 1.85 Mpro   6ZP2 3.10 Spike   
6XB0 1.8 Mpro   6ZP5 3.10 Spike   
6XHU 1.8 Mpro   6ZP7 3.30 Spike   
6XKF 1.8 Mpro   6ZWV 3.50 Spike   
6XKH 1.28 Mpro   7BWJ 2.85 Spike (RBD)   
6M03 Mpro   7BYR 3.84 Spike   
6M2Q 1.7 Mpro   7BZ5 1.84 Spike (RBD)   
5R8T 1.27 Mpro   7C01 2.88 Spike (S1)   
6XA4 1.65 Mpro UAW241 Surface 7C2L 3.10 Spike   
6XBG 1.45 Mpro UAW246 7C8V 2.15 Spike (RBD)   
6XBH 1.6 Mpro UAW247 7C8W 2.77 Spike (RBD)   
6XBI 1.7 Mpro UAW248 7CAH 3.90 Spike (RBD)   
6XFN 1.7 Mpro UAW243 7CAN 2.94 Spike (RBD)   
6Y2E 1.75 Mpro   6VWW 2.20 NSP15   
6Y84 1.39 Mpro   6W01 1.90 NSP15   
6YB7 1.25 Mpro   6X1B 1.97 NSP15   
7BRO Mpro   6XDH 2.35 NSP15   
7JFQ 1.55 Mpro   6WLC 1.82 NSP15 U5P NendoU active site 
6XEZ 3.50 NSP13   6WXC 1.85 NSP15 CMU 
6ZSL 1.94 NSP13   6X4I 1.85 NSP15 U3P 
 7JTL 2.04 ORF8   
PDBResolution (Å)Target NameLigand IDCavityPDB IDResolution (Å)Target NameLigand IDCavity
6WQ3 2.10 NSP16 SAH SAM binding site 6ZME 3.00 NSP1   
6WRZ 2.25 NSP16 SAH 6ZLW 2.60 NSP1   
6WVN 2.00 NSP16 SAM 6ZM7 2.70 NSP1   
6XKM 2.25 NSP16 SAM 6ZMT 3.00 NSP1   
6W61 2.00 NSP16 SAM 6ZN5 3.20 NSP1   
6WJT 2.00 NSP16 SAH 6ZMI 2.60 NSP1   
6WKS 1.80 NSP16 SAM 6ZMO 3.10 NSP1   
6W4H 1.80 NSP16 SAM 6ZOJ 2.80 NSP1   
6W75 1.95 NSP16 SAM 6ZP4 2.90 NSP1   
6WKQ 1.98 NSP16 SFG 6ZOK 2.80 NSP1   
6YZ1 2.40 NSP16 SFG 6ZON 3.00 NSP1   
7BQ7 2.37 NSP16 SAM 6WQ3 2.10 NSP10   
5REG 1.67 Mpro LWA Allosteric cavity 1 6WRZ 2.25 NSP10   
5RF8 1.44 Mpro SFY 6WVN 2.00 NSP10   
5RE7 1.79 Mpro T0S Allosteric cavity 2 6XKM 2.25 NSP10   
5RE8 1.81 Mpro T0V 6W4H 1.80 NSP10   
5RF4 1.61 Mpro T5Y 6W61 2.00 NSP10   
5RF9 1.43 Mpro S7D 6W75 1.95 NSP10   
5RFD 1.41 Mpro T6J 6WJT 2.00 NSP10   
5RGJ 1.34 Mpro U0S 6WKS 1.80 NSP10   
6XB1 1.8 Mpro NEN Allosteric cavity 3 6WKQ 1.98 NSP10   
6XB2 2.1 Mpro NEN 6YZ1 2.40 NSP10   
5REC 1.73 Mpro T1J Allosteric cavity 4 6ZCT 2.55 NSP10   
5REE 1.77 Mpro T1M 7BQ7 2.37 NSP10   
5RGS 1.72 Mpro S7V 6WQD 1.95 NSP7   
5RE5 2.07 Mpro T0J Allosteric cavity 5 6WTC 1.85 NSP7   
5RE6 1.87 Mpro O0S 6XEZ 3.50 NSP7   
5REA 1.63 Mpro JGP Allosteric cavity 6 6XIP 1.50 NSP7   
5RFB 1.48 Mpro K3S 6XQB 3.40 NSP7   
5RFC 1.4 Mpro K1Y 6WIQ 2.85 NSP7   
5RGG 2.26 Mpro NZD 7C2K 2.93 NSP7   
6YVF 1.6 Mpro A82 7BTF 2.95 NSP7   
6WNP 1.44 Mpro U5G Active site 7BV1 2.80 NSP7   
5R7Y 1.65 Mpro JFM  7BV2 2.50 NSP7   
5R7Z 1.59 Mpro HWH  7BW4 3.70 NSP7   
5R80 1.93 Mpro RZG  7BZF 3.26 NSP7   
5R81 1.95 Mpro RZJ  6YYT 2.90 NSP7   
5R82 1.31 Mpro RZS  6M5I 2.49 NSP7   
5R83 1.58 Mpro K0G  6M71 2.90 NSP7   
5R84 1.83 Mpro GWS  6WQD 1.95 NSP8 (C-terminal)   
5RE4 1.88 Mpro SZY  6WTC 1.85 NSP8 (C-terminal)   
5RE9 1.72 Mpro LPZ  6XEZ 3.50 NSP8   
5REB 1.68 Mpro T0Y  6XIP 1.50 NSP8 (C-terminal)   
5REF 1.61 Mpro 6SU  6XQB 3.40 NSP8   
5REH 1.8 Mpro AWP  6WIQ 2.85 NSP8 (C-terminal)   
5REJ 1.72 Mpro T1V  7C2K 2.93 NSP8   
5REK 1.74 Mpro T1Y  7BTF 2.95 NSP8   
5REL 1.62 Mpro T2G  7BV1 2.80 NSP8   
5REM 1.96 Mpro T2J  7BV2 2.50 NSP8   
5REN 2.15 Mpro T2V  7BW4 3.70 NSP8   
5REO 1.88 Mpro T2Y  7BZF 3.26 NSP8   
5REP 1.81 Mpro T3G  6YYT 2.90 NSP8   
5RER 1.88 Mpro T3J  6M5I 2.49 NSP8   
5RES 1.65 Mpro T3V  6M71 2.90 NSP8   
5RET 1.68 Mpro T47  6WXD 2.00 NSP9   
5REU 1.69 Mpro T4D  6W4B 2.95 NSP9   
5REV 1.6 Mpro T4J  6WC1 2.40 NSP9   
5REW 1.55 Mpro T4M  6M3M 2.70 Nucleocapsid (N-terminal)   
5REX 2.07 Mpro T4V  6VYO 1.70 Nucleocapsid (N-terminal)   
5REY 1.96 Mpro T4Y  6WJI 2.05 Nucleocapsid (C-terminal)   
5REZ 1.79 Mpro T54  6WKP 2.67 Nucleocapsid (N-terminal)   
5RF0 1.65 Mpro T5D  6WZO 1.42 Nucleocapsid (N-terminal)   
5RF1 1.73 Mpro T5G  6WZQ 1.45 Nucleocapsid (N-terminal)   
5RF2 1.53 Mpro HVB  6YUN 1.44 Nucleocapsid (C-terminal)   
5RF3 1.5 Mpro T5V  6ZCO 1.36 Nucleocapsid (C-terminal)   
5RF6 1.45 Mpro NTG  7C22 2.00 Nucleocapsid (C-terminal)   
5RF7 1.54 Mpro T67  6XDC 2.90 ORF3a   
5RFA 1.52 Mpro JGY  6W37 2.90 ORF7a   
5RFE 1.46 Mpro JGG  6Z4U 1.95 ORF9b   
5RFF 1.78 Mpro T6M  6WOJ 2.20 NSP3 (ADRP) APR ADP-ribose binding site 
5RFG 2.32 Mpro T6V  6W02 1.50 NSP3 (ADRP) APR 
5RFH 1.58 Mpro T6Y  6W6Y 1.45 NSP3 (ADRP) AMP 
5RFI 1.69 Mpro T71  6WCF 1.06 NSP3 (ADRP)   
5RFJ 1.8 Mpro T7A  6WEN 1.35 NSP3 (ADRP)   
5RFK 1.75 Mpro T7D  6WEY 0.95 NSP3 (ADRP)   
5RFL 1.64 Mpro T7G  6YWK 2.20 NSP3 (ADRP)   
5RFM 2.06 Mpro T7J  6YWM 2.16 NSP3 (ADRP)   
5RFN 1.8 Mpro T7P  6VXS 2.03 NSP3 (ADRP)   
5RFO 1.83 Mpro T7S  6YWL 2.50 NSP3 (ADRP)   
5RFP 2.03 Mpro T7V  6WZU 1.79 NSP3 (PLpro)   
5RFQ 1.76 Mpro T7Y  6WRH 1.60 NSP3 (PLpro)   
5RFR 1.71 Mpro T81  6XA9 2.90 NSP3 (PLpro)   
5RFS 1.7 Mpro T84  6XAA 2.70 NSP3 (PLpro)   
5RFT 1.58 Mpro T8A  6XG3 2.48 NSP3 (PLpro)   
5RFU 1.53 Mpro T8D  6YVA 3.18 NSP3 (PLpro)   
5RFV 1.48 Mpro T8J  6W9C 2.70 NSP3 (PLpro)   
5RFW 1.43 Mpro T8M  6WUU 2.79 NSP3 (PLpro) VIR250 Catalytic site 
5RFX 1.55 Mpro T8P  6WX4 1.65 NSP3 (PLpro) VIR251 
5RFY 1.9 Mpro T8S  7JIR 2.09 NSP3 (PLpro) TTT PLpro active site 
5RFZ 1.68 Mpro T8V  7JIT 1.95 NSP3 (PLpro) Y95 
5RG0 1.72 Mpro T8Y  7JIV 2.05 NSP3 (PLpro) VBY 
5RG1 1.65 Mpro T9J  7JIW 2.30 NSP3 (PLpro) VBY 
5RG2 1.63 Mpro T9M  7JN2 1.93 NSP3 (PLpro) Y41 
5RG3 1.58 Mpro T9P  6M71 2.90 RdRp   
5RGH 1.7 Mpro U0M  6XEZ 3.50 RdRp   
5RGI 1.57 Mpro U0P  6XQB 3.40 RdRp   
5RGK 1.43 Mpro U0V  6YYT 2.90 RdRp   
5RGL 1.76 Mpro U0Y  7BTF 2.95 RdRp   
5RGM 2.04 Mpro U1D  7BV1 2.80 RdRp   
5RGN 1.86 Mpro U1A  7BV2 2.50 RdRp F86 Catalytic active site 
5RGO 1.74 Mpro U1G  7BW4 3.70 RdRp   
5RGP 2.07 Mpro U1M  7BZF 3.26 RdRp   
5RGQ 2.15 Mpro U1V  7C2K 2.93 RdRp   
5RGT 2.22 Mpro UHS  6LVN 2.47 Spike (HR domain)   
5RGU 2.1 Mpro UGD  6LXT 2.90 Spike (S2)   
5RGV 1.82 Mpro UGG  6LZG 2.50 Spike (RBD)   
5RGW 1.43 Mpro UGM  6M0J 2.45 Spike (RBD)   
5RGX 1.69 Mpro UGP  6M17 2.90 Spike (RBD)   
5RGY 1.97 Mpro UGS  6M1V 1.50 Spike (S2)   
5RGZ 1.52 Mpro UH1  6VSB 3.46 Spike   
5RH0 1.91 Mpro UH4  6VW1 2.68 Spike (RBD)   
5RH1 1.96 Mpro UGV  6VXX 2.80 Spike   
5RH2 1.82 Mpro UH7  6VYB 3.20 Spike   
5RH3 1.69 Mpro UHA  6W41 3.08 Spike (S1)   
5RH5 1.72 Mpro UHV  6WPS 3.10 Spike   
5RH6 1.6 Mpro UHY  6WPT 3.70 Spike   
5RH7 1.71 Mpro UJ1  6X29 2.70 Spike   
5RH8 1.81 Mpro UHM  6X2A 3.30 Spike   
5RH9 1.91 Mpro UJ4  6X2B 3.60 Spike   
5RHA 1.51 Mpro T8M  6X2C 3.20 Spike   
5RHB 1.43 Mpro USD  6X6P 3.22 Spike   
5RHC 1.58 Mpro USA  6X79 2.90 Spike   
5RHD 1.57 Mpro US7  6XC2 3.11 Spike (S1)   
5RHE 1.56 Mpro UPD  6XC3 2.69 Spike (S1)   
5RHF 1.76 Mpro UPJ  6XC4 2.34 Spike (S1)   
6LU7 2.16 Mpro N3  6XC7 2.88 Spike (S1)   
6LZE 1.5 Mpro FHR  6XCM 3.42 Spike   
6M0K 1.5 Mpro FJC  6XCN 3.66 Spike   
6M2N 2.19 Mpro 3WL  6XDG 3.90 Spike (RBD)   
6W63 2.1 Mpro X77  6XE1 2.75 Spike (S1)   
6W9Q 2.05 Mpro PO4  6XEY 3.25 Spike   
6WTJ 1.9 Mpro K36  6XKL 3.21 Spike   
6WTK Mpro UED  6XLU 2.40 Spike   
6WTT 2.15 Mpro K36  6XM0 2.70 Spike   
6WTT 2.15 Mpro K36  6XM3 2.90 Spike   
6XCH 2.2 Mpro Leupeptin  6XM4 2.90 Spike   
6XHM 1.4 Mpro V2M  6XM5 3.10 Spike   
6XMK 1.7 Mpro QYS  6XR8 2.90 Spike   
6XOA 2.1 Mpro EDO  6XRA 3.00 Spike   
6XQS 1.9 Mpro SV6  6XS6 3.70 Spike   
6XQT 2.3 Mpro NNA  6YLA 2.42 Spike (RBD)   
6XQU 2.2 Mpro U5G  6YM0 4.36 Spike (RBD)   
6XR3 1.45 Mpro V7G  6YOR 3.30 Spike (S1)   
6Y2F 1.95 Mpro O6K  6YZ5 1.80 Spike (RBD)   
6Y2G 2.2 Mpro O6K  6YZ7 3.30 Spike (RBD)   
6YNQ 1.8 Mpro P6N  6Z2M 2.71 Spike (RBD)   
6YT8 2.05 Mpro PK8  6Z43 3.30 Spike   
6YZ6 1.7 Mpro Leupeptin  6Z97 3.40 Spike   
6ZRT 2.1 Mpro SV6  6ZBP 1.85 Spike (RBD)   
6ZRU 2.1 Mpro U5G  6ZCZ 2.65 Spike (RBD)   
7BQY 1.7 Mpro N3  6ZDG 4.70 Spike (ectodomain)   
7BRP 1.8 Mpro HU5  6ZDH 3.70 Spike   
7BRR 1.4 Mpro K36  6ZER 3.80 Spike (RBD)   
7BUY 1.6 Mpro JRY  6ZFO 4.40 Spike (ectodomain)   
7C7P 1.74 Mpro SV6  6ZGE 2.60 Spike   
7C8R 2.3 Mpro TG3  6ZGG 3.80 Spike   
7C8T 2.05 Mpro NOL  6ZGH 6.80 Spike   
7C8U 2.35 Mpro K36  6ZGI 2.90 Spike   
7COM 2.25 Mpro U5G  6ZHD 3.70 Spike   
5RED 1.47 Mpro JJG Allosteric cavity 8 6ZOW 3.00 Spike   
5REI 1.82 Mpro T1S 6ZOX 3.00 Spike   
5RF5 1.74 Mpro HV2 6ZOY 3.10 Spike   
5RGR 1.41 Mpro K1G 6ZOZ 3.50 Spike   
5RH4 1.34 Mpro UHG 6ZP0 3.00 Spike   
6WQF 2.3 Mpro   6ZP1 3.30 Spike   
6WTM 1.85 Mpro   6ZP2 3.10 Spike   
6XB0 1.8 Mpro   6ZP5 3.10 Spike   
6XHU 1.8 Mpro   6ZP7 3.30 Spike   
6XKF 1.8 Mpro   6ZWV 3.50 Spike   
6XKH 1.28 Mpro   7BWJ 2.85 Spike (RBD)   
6M03 Mpro   7BYR 3.84 Spike   
6M2Q 1.7 Mpro   7BZ5 1.84 Spike (RBD)   
5R8T 1.27 Mpro   7C01 2.88 Spike (S1)   
6XA4 1.65 Mpro UAW241 Surface 7C2L 3.10 Spike   
6XBG 1.45 Mpro UAW246 7C8V 2.15 Spike (RBD)   
6XBH 1.6 Mpro UAW247 7C8W 2.77 Spike (RBD)   
6XBI 1.7 Mpro UAW248 7CAH 3.90 Spike (RBD)   
6XFN 1.7 Mpro UAW243 7CAN 2.94 Spike (RBD)   
6Y2E 1.75 Mpro   6VWW 2.20 NSP15   
6Y84 1.39 Mpro   6W01 1.90 NSP15   
6YB7 1.25 Mpro   6X1B 1.97 NSP15   
7BRO Mpro   6XDH 2.35 NSP15   
7JFQ 1.55 Mpro   6WLC 1.82 NSP15 U5P NendoU active site 
6XEZ 3.50 NSP13   6WXC 1.85 NSP15 CMU 
6ZSL 1.94 NSP13   6X4I 1.85 NSP15 U3P 
 7JTL 2.04 ORF8   
a

NSP16: 2′-O-methyltransferase; Mpro: main protease; 3CLpro: 3C-like proteinase; RdRp: RNA-directed RNA polymerase; NSP15: uridylate-specific endoribonuclease; PLpro: papain-like protease.

Some of the structures reported in Table 1.3 have co-crystallized ligands, which can provide information about active site of the enzyme or receptor along with key residues involved in the ligand binding. The common residues that bind to multiple ligands can provide clues for designing molecules that can target these residues. Furthermore, information on co-crystallized ligands for a target can be utilized to develop pharmacophore-based models for screening of drug databases.

Structure-based approaches mainly involve molecular docking, molecular dynamics (MD) and free energy of binding calculations for drug–receptor complexes. As an effort to find repurposed drugs using in silico methods, some groups have shared their results in a database (https://covid.molssi.org/, https://shennongproject.ai/). Other groups have contributed in terms of providing a COVID-19 docking server where researchers can submit their compounds for in silico screening (http://ncov.schanglab.org.cn). A similar resource is the D3Targets-2019-nCoV server, where target prediction for drugs is also provided along with docking scores (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). The coronavirus antiviral research database (https://covdb.stanford.edu//) is another example which contains candidate antiviral compounds as well as molecular targets of SARS-CoV-2.

Several drug databases that are used for screening compounds against SARS-CoV-2 therapeutic targets for drug repurposing are listed in Table 1.4.

Table 1.4

Databases used for virtual screening against SARS-CoV-2

DatabaseActive linkAbout
FDA-Approved Drugs Database https://www.accessdata.fda.gov/scripts/cder/daf/ Includes information about drugs approved for human use in the United States 
DrugBank http://www.drugbank.ca Details of approved drugs (pharmaceuticals and chemicals) 
ZINC Database http://zinc.docking.org/ Compilation of commercially available chemical compounds provided by the Shoichet Laboratory, University of California, San Francisco (UCSF) 
ChEMBL https://www.ebi.ac.uk/chembl/ Database of small molecules, their interactions and effects on targets 
PubChem https://pubchem.ncbi.nlm.nih.gov/ Database of chemical compounds developed by the National Center for Biotechnology Information (NCBI) 
ChemIDPlus https://chem.nlm.nih.gov/chemidplus/ Database of chemical compounds and structures from the U.S. National Library of Medicine. 
SelleckChem https://www.selleckchem.com/screening-libraries.html Database of bioactive compounds including FDA-approved drugs and those that have completed phase 1 trials 
SWEETLEAD https://simtk.org/projects/sweetlead Database of approved medicines, illegal drugs and isolates from traditional medicinal herbs 
NCGC Pharmaceutical Collection https://tripod.nih.gov/npc/ Collection of approved and investigational drugs for high-throughput screening 
e-Drug3d https://chemoinfo.ipmc.cnrs.fr/MOLDB/index.php FDA-approved drugs and active metabolites 
DrugCentral https://drugcentral.org/ Resource for FDA approved drugs and drugs approved outside United States 
CureFFI database https://www.cureffi.org/2013/10/04/list-of-fda-approved-drugs-and-cns-drugs-with-smiles/ List of FDA-approved drugs and central nervous system drugs 
BindingDB https://www.bindingdb.org/bind/index_original.jsp Database of measured binding affinities 
SwissSimilarity http://www.swisssimilarity.ch/ Library of drugs and bioactive compounds 
DatabaseActive linkAbout
FDA-Approved Drugs Database https://www.accessdata.fda.gov/scripts/cder/daf/ Includes information about drugs approved for human use in the United States 
DrugBank http://www.drugbank.ca Details of approved drugs (pharmaceuticals and chemicals) 
ZINC Database http://zinc.docking.org/ Compilation of commercially available chemical compounds provided by the Shoichet Laboratory, University of California, San Francisco (UCSF) 
ChEMBL https://www.ebi.ac.uk/chembl/ Database of small molecules, their interactions and effects on targets 
PubChem https://pubchem.ncbi.nlm.nih.gov/ Database of chemical compounds developed by the National Center for Biotechnology Information (NCBI) 
ChemIDPlus https://chem.nlm.nih.gov/chemidplus/ Database of chemical compounds and structures from the U.S. National Library of Medicine. 
SelleckChem https://www.selleckchem.com/screening-libraries.html Database of bioactive compounds including FDA-approved drugs and those that have completed phase 1 trials 
SWEETLEAD https://simtk.org/projects/sweetlead Database of approved medicines, illegal drugs and isolates from traditional medicinal herbs 
NCGC Pharmaceutical Collection https://tripod.nih.gov/npc/ Collection of approved and investigational drugs for high-throughput screening 
e-Drug3d https://chemoinfo.ipmc.cnrs.fr/MOLDB/index.php FDA-approved drugs and active metabolites 
DrugCentral https://drugcentral.org/ Resource for FDA approved drugs and drugs approved outside United States 
CureFFI database https://www.cureffi.org/2013/10/04/list-of-fda-approved-drugs-and-cns-drugs-with-smiles/ List of FDA-approved drugs and central nervous system drugs 
BindingDB https://www.bindingdb.org/bind/index_original.jsp Database of measured binding affinities 
SwissSimilarity http://www.swisssimilarity.ch/ Library of drugs and bioactive compounds 

The in silico structure-based drug repurposing studies for various SARS-CoV-2 targets that are reported in the literature are summarized in this section.

Among all SARS-CoV-2 targets, the main protease (Mpro) is the most studied target for in silico repurposing. An important reason for this is availability of structures (using X-ray, electron microscopy, NMR, etc.) with and without co-crystallized ligands. These structures have been utilized by several researchers to identify repurposing opportunities. There are some drugs for which median inhibitory concentration (IC50) in the Mpro assay has been reported,22–25  which are discussed herein. In addition, we refer to the cellular activity of drugs against SARS-CoV-2 infection reported in the literature8–10  and on the website https://ghddi-ailab.github.io/Targeting2019-nCoV/preclinical/. A summary of structure-based studies conducted so far is presented, based on crystal structure or models used, docking programs used, drugs and databases screened, MD program used, duration of MD simulation and free-energy estimation method used. There are 52 publications reporting in silico drug repurposing efforts specifically for the Mpro target, and their compilation using these parameters is provided in Table 1.5.

Table 1.5

Summary of Mpro studiesa

PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6M03 DrugBank AutoDock Vina    Glecaprevir, maraviroc 26  
6LU7 Antivirals, statins AutoDock Vina    Pitavastatin, rosuvastatin, fluvastatinb 27  
6LU7 Thiol-reacting FDA-approved drugs AutoDock Vina    Disulfiramc, captopril 29  
6LU7 19 in vitro tested or in trial for COVID-19 AutoDock Vina NAMD 5 ns/125 ns Nil Entecavir, nelfinavir 31  
6LU7, 6M2N Protease inhibitors AutoDock    Telaprevir, boceprevir, lopinavir, edoxaban, ritonavir, argatroban, sitagliptin, inogatran. 32  
6LU7 7 HIV-1 protease inhibitors VINA/VegaZZ NAMD 1 ns  Saquinavir 33  
6LU7 DrugBank Glide AMBER 1 ns/100 ns MM-PBSA-WSAS Carfilzomib, eravacycline, valrubicin, lopinavir, elbasvir 34  
6Y2G ChemBL AutoDock Vina, RDOCK    Eszopiclone 35  
6LU7 Antimalarial Glide GROMACS 20 ns  Amodiaquine 36  
5R7Y, 5R7Z, 5R80, 5R81, 5R82 61 antiviral drugs Glide    Lopinavir, asunaprevir, remdesivir, indinavir, ritonavir, galidesivir 37  
6Y2G 61 antiviral drugs AutoDock    Artemisinin, ritonavir, darunavir, lopinavir, vitamin D, vitamin E 38  
6Y2F 22 flavonoids, antimalarials, vitamins AutoDock Vina YASARA 10 ns  Lopinavirritonavir, tipranavir, raltegravir 39  
6M03, 6LU7, 6Y2F/6Y2G 75 antiviral, anticancer, antimalarial Glide Desmond 200 ns MM-GBSA Leupeptin, pepstatin A, nelfinavir, birinapant, lypressin, octreotide 40  
6Y84 SelleckChem, DrugBank, Repurposing hub SWISS-DOCK    Emetine, hesperidin, indinavir, itraconazole 41  
6LU7 33 antinematodes antiprotozoals, antifungals, natural products, antivirals GOLD YASARA 100 ns MM-PBSA Simeprevir, ergotamine, bromocriptine, tadalafil 42  
HM 1615 FDA-approved drugs from ZINC iDock GROMACS 100 ns  Daunorubicin, ergotamine, ergoloid, bromocriptine, meclocycline, amrubicin, 43  
6W63 SuperDRUG2 Glide   MM-GBSA Binifibrate, macimorelin, bamifylline, rilmazafone, afatinib, ezetimibe 44  
6LU7 OTAVA Chemicals (1017 compounds), Machine Learning SARS Targeted Library (1577 compounds) GOLD    Apixaban 45  
6LU7 606 million ZINC AutoDock Vina, AutoDock, Glide Desmond 20 ns MM-GBSA Apixaban, nelfinavir, glecaprevir, rivaroxaban, betrixaban, amprenavir 46  
6LU7 SelleckChem Glide    Ribavirin, telbivudine, aminosalicylate, pyrazinamide, vitamin B12, nicotinamide, temozolomide 47  
HM 7173 DrugsLib MTiOpenScreen, AutoDock Vina    Velpatasvir, ledipasvir 48  
6LU7 31 anti-HIV drugs Glide AMBER 100 ns MM-GBSA Saquinavir 49  
6W63 DrugBank Glide AMBER 100 ns MM-PBSA Ritonavir, nelfinavir, leuprolide, valrubicin, teniposide 50  
6LU7 16 antiviral drugs MOE AMBER 20 ns MM (GB/PB) SA Remdesivir, saquinavir, darunavir 51  
6LU7 DrugBank AutoDock AMBER 10 ns MM-GBSA Cobicistat, darunavir 52  
6LU7 10 000 approved, drugs in clinical trials and natural products Glide, iFitDock    Disulfiram, cinanserin, tideglusib, shikonin, ebselen 53  
6LU7 55 antiviral, antimicrobial drugs AutoDock    Pimozide, ebastine, bepridil 54  
6LU7 DrugBank AutoDock Vina    Mezlocillin, camazepam, spirapril, rolitetracycline, rescinnamine, tolvaptan, ciclesonide, raltegravir 55  
6W63 3981 approved drugs from FDA, EMA DockThorAutoDock Vina    Niaprazine, tolazamide, gliclazide, glibenclamide, miconazole, econazole, sulconazole, tioconazole, efinaconazole, bedaquiline 56  
6LU7 1930 FDA-approved drugs and active metabolites Glide, Fred, AutoDock Vina    Perampanel, carprofen, celecoxib, alprazolam, trovafloxacin, sarafloxacin, ethyl biscoumacetate 57  
6W63 Natural products, protease inhibitors and FDA-approved drugs Glide  100 ns MM-GBSA Saquinavir, atazanavir, nelfinavir, lopinavir, indinavir, acarbose, octreotide, colistin 58  
6LU7 232 drugs similar to efavirenz AutoDock NAMD   Simvastatin, lovastatin, oxacillin, podophyllotoxin, gefitinib 59  
6Y2F ZINC, Spec database Glide GROMACS 100 ns MM-PBSA Fortovase, cobicistat, cangrelor, lopromide 60  
6LU7 SelleckChem, DrugBank FlexX Desmond 100 ns MM-GBSA Birinapant, dynasore, mitoxantrone, leucovorin 61  
6LU7 23 antiviral drugs AutoDock Vina YASARA 50 ns MM-PBSA Epirubicin, vapreotide, saquinavir 62  
6LU7 2683 SelleckChem MOE, AutoDock Vina    Oxytetracycline, naringin, kanamycin, cefpiramide, salvianolic acid, teniposide, etoposide, doxorubicin 63  
6LU7 6 drugs in clinical trials AutoDock Vina    Azithromycin, baricitinib, quinacrine, ruxolitinib 64  
6Y2F Antiviral drugs and phytochemicals AutoDock Vina    Simeprevir, ledipasvir, paritaprevir, glecaprevir, daclatasvir, atazanavir 65  
6LU7 Approved drugs, Asinex BioDesign Library Glide    Ritonavir, nelfinavir, saquinavir pralmorelin, iodixanol, iotrolan 66  
6LU7 33 approved protease inhibitors AutoDock Vina    Paritaprevir, ciluprevir, simeprevir, deldeprevir, indinavir, saquinavir, faldaprevir, brecanavir, grazoprevir, lopinavir 67  
6LU7 1615 FDA-approved drugs from ZINC15 AutoDock Vina YASARA 7 ns  Perampanel, conivaptan, sonidegib, azelastine, idelalisib, suvorexant, olaparib, ponatinib, loxapine, tolvaptan 68  
6M03 129 antimalarial, antiparasitic, antibiotics, or antivirals and 992 ZINC Natural Products database AutoDock Vina, AutoDock    Eprinomectin, fexofenadine, artefenomel, doramectin, betulinic acid, atovaquone, tetrandrine 69  
6YB7 19 antiviral and 10 natural ligands     Ritonavir 70  
6LU7 4 tetracyclines AutoDock Vina Desmond 100 ns  Doxycycline, tetracycline, demeclocycline, minocycline 71  
6LU7 1930 FDA-approved drugs plus active metabolites PLANTS GROMACS 100 ns MM-PBSA Triptorelin, nafarelin, icatibant, cobicistat, histrelin 72  
6LU7 5881 Drugs approved by FDA and anywhere in the world AutoDock GROMACS 100 ns MM-PBSA Docetaxel, palbociclib, ergoloid, cabazitaxel, imatinib, alectinib, simeprevir, azelastine, plerixafor, dasabuvir, paclitaxel, clofazimine, rupatadine, pyronaridine, sulforidazine, thioproperazine, desmethylazelastine 73  
6LU7 FDA-approved and SWEETLEAD DOCK 6 AMBER 50 ns  Indinavir, ceftin, ivermectin, ceftiofur, cefazedone, amprenavir, neomycin, ceftazidime, ceftizoxime 74  
6LU7 1 million ZINC AutoDock Vina GROMACS 100 ns  Nilotinib, naldemedine, enasidenib, afatinib, ertapenem, ebselen, disulfiram, carmofur, cinanserin, shikonin, tideglusib 75  
6LU7 FDA-approved covalent binders Covdock GROMACS 50 ns  Saquinavir, ritonavir, oseltamivir remdesivir, delavirdine, cefuroxime, prevacid 76  
6LU7 124 antimicrobial drugs Cdocker GROMACS 100 ns  Viomycin, bacampicillin 77  
6Y2E 124 antivirals and protease inhibitors AutoDock Vina    Edoxaban, apixaban, betrixaban 78  
6Y2F 8000 approved and experimental drugs Glide    Fosamprenavir, darunavir, amprenavir, tipranavir, nicotinamide adenine dinucleotide (NAD) 79  
PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6M03 DrugBank AutoDock Vina    Glecaprevir, maraviroc 26  
6LU7 Antivirals, statins AutoDock Vina    Pitavastatin, rosuvastatin, fluvastatinb 27  
6LU7 Thiol-reacting FDA-approved drugs AutoDock Vina    Disulfiramc, captopril 29  
6LU7 19 in vitro tested or in trial for COVID-19 AutoDock Vina NAMD 5 ns/125 ns Nil Entecavir, nelfinavir 31  
6LU7, 6M2N Protease inhibitors AutoDock    Telaprevir, boceprevir, lopinavir, edoxaban, ritonavir, argatroban, sitagliptin, inogatran. 32  
6LU7 7 HIV-1 protease inhibitors VINA/VegaZZ NAMD 1 ns  Saquinavir 33  
6LU7 DrugBank Glide AMBER 1 ns/100 ns MM-PBSA-WSAS Carfilzomib, eravacycline, valrubicin, lopinavir, elbasvir 34  
6Y2G ChemBL AutoDock Vina, RDOCK    Eszopiclone 35  
6LU7 Antimalarial Glide GROMACS 20 ns  Amodiaquine 36  
5R7Y, 5R7Z, 5R80, 5R81, 5R82 61 antiviral drugs Glide    Lopinavir, asunaprevir, remdesivir, indinavir, ritonavir, galidesivir 37  
6Y2G 61 antiviral drugs AutoDock    Artemisinin, ritonavir, darunavir, lopinavir, vitamin D, vitamin E 38  
6Y2F 22 flavonoids, antimalarials, vitamins AutoDock Vina YASARA 10 ns  Lopinavirritonavir, tipranavir, raltegravir 39  
6M03, 6LU7, 6Y2F/6Y2G 75 antiviral, anticancer, antimalarial Glide Desmond 200 ns MM-GBSA Leupeptin, pepstatin A, nelfinavir, birinapant, lypressin, octreotide 40  
6Y84 SelleckChem, DrugBank, Repurposing hub SWISS-DOCK    Emetine, hesperidin, indinavir, itraconazole 41  
6LU7 33 antinematodes antiprotozoals, antifungals, natural products, antivirals GOLD YASARA 100 ns MM-PBSA Simeprevir, ergotamine, bromocriptine, tadalafil 42  
HM 1615 FDA-approved drugs from ZINC iDock GROMACS 100 ns  Daunorubicin, ergotamine, ergoloid, bromocriptine, meclocycline, amrubicin, 43  
6W63 SuperDRUG2 Glide   MM-GBSA Binifibrate, macimorelin, bamifylline, rilmazafone, afatinib, ezetimibe 44  
6LU7 OTAVA Chemicals (1017 compounds), Machine Learning SARS Targeted Library (1577 compounds) GOLD    Apixaban 45  
6LU7 606 million ZINC AutoDock Vina, AutoDock, Glide Desmond 20 ns MM-GBSA Apixaban, nelfinavir, glecaprevir, rivaroxaban, betrixaban, amprenavir 46  
6LU7 SelleckChem Glide    Ribavirin, telbivudine, aminosalicylate, pyrazinamide, vitamin B12, nicotinamide, temozolomide 47  
HM 7173 DrugsLib MTiOpenScreen, AutoDock Vina    Velpatasvir, ledipasvir 48  
6LU7 31 anti-HIV drugs Glide AMBER 100 ns MM-GBSA Saquinavir 49  
6W63 DrugBank Glide AMBER 100 ns MM-PBSA Ritonavir, nelfinavir, leuprolide, valrubicin, teniposide 50  
6LU7 16 antiviral drugs MOE AMBER 20 ns MM (GB/PB) SA Remdesivir, saquinavir, darunavir 51  
6LU7 DrugBank AutoDock AMBER 10 ns MM-GBSA Cobicistat, darunavir 52  
6LU7 10 000 approved, drugs in clinical trials and natural products Glide, iFitDock    Disulfiram, cinanserin, tideglusib, shikonin, ebselen 53  
6LU7 55 antiviral, antimicrobial drugs AutoDock    Pimozide, ebastine, bepridil 54  
6LU7 DrugBank AutoDock Vina    Mezlocillin, camazepam, spirapril, rolitetracycline, rescinnamine, tolvaptan, ciclesonide, raltegravir 55  
6W63 3981 approved drugs from FDA, EMA DockThorAutoDock Vina    Niaprazine, tolazamide, gliclazide, glibenclamide, miconazole, econazole, sulconazole, tioconazole, efinaconazole, bedaquiline 56  
6LU7 1930 FDA-approved drugs and active metabolites Glide, Fred, AutoDock Vina    Perampanel, carprofen, celecoxib, alprazolam, trovafloxacin, sarafloxacin, ethyl biscoumacetate 57  
6W63 Natural products, protease inhibitors and FDA-approved drugs Glide  100 ns MM-GBSA Saquinavir, atazanavir, nelfinavir, lopinavir, indinavir, acarbose, octreotide, colistin 58  
6LU7 232 drugs similar to efavirenz AutoDock NAMD   Simvastatin, lovastatin, oxacillin, podophyllotoxin, gefitinib 59  
6Y2F ZINC, Spec database Glide GROMACS 100 ns MM-PBSA Fortovase, cobicistat, cangrelor, lopromide 60  
6LU7 SelleckChem, DrugBank FlexX Desmond 100 ns MM-GBSA Birinapant, dynasore, mitoxantrone, leucovorin 61  
6LU7 23 antiviral drugs AutoDock Vina YASARA 50 ns MM-PBSA Epirubicin, vapreotide, saquinavir 62  
6LU7 2683 SelleckChem MOE, AutoDock Vina    Oxytetracycline, naringin, kanamycin, cefpiramide, salvianolic acid, teniposide, etoposide, doxorubicin 63  
6LU7 6 drugs in clinical trials AutoDock Vina    Azithromycin, baricitinib, quinacrine, ruxolitinib 64  
6Y2F Antiviral drugs and phytochemicals AutoDock Vina    Simeprevir, ledipasvir, paritaprevir, glecaprevir, daclatasvir, atazanavir 65  
6LU7 Approved drugs, Asinex BioDesign Library Glide    Ritonavir, nelfinavir, saquinavir pralmorelin, iodixanol, iotrolan 66  
6LU7 33 approved protease inhibitors AutoDock Vina    Paritaprevir, ciluprevir, simeprevir, deldeprevir, indinavir, saquinavir, faldaprevir, brecanavir, grazoprevir, lopinavir 67  
6LU7 1615 FDA-approved drugs from ZINC15 AutoDock Vina YASARA 7 ns  Perampanel, conivaptan, sonidegib, azelastine, idelalisib, suvorexant, olaparib, ponatinib, loxapine, tolvaptan 68  
6M03 129 antimalarial, antiparasitic, antibiotics, or antivirals and 992 ZINC Natural Products database AutoDock Vina, AutoDock    Eprinomectin, fexofenadine, artefenomel, doramectin, betulinic acid, atovaquone, tetrandrine 69  
6YB7 19 antiviral and 10 natural ligands     Ritonavir 70  
6LU7 4 tetracyclines AutoDock Vina Desmond 100 ns  Doxycycline, tetracycline, demeclocycline, minocycline 71  
6LU7 1930 FDA-approved drugs plus active metabolites PLANTS GROMACS 100 ns MM-PBSA Triptorelin, nafarelin, icatibant, cobicistat, histrelin 72  
6LU7 5881 Drugs approved by FDA and anywhere in the world AutoDock GROMACS 100 ns MM-PBSA Docetaxel, palbociclib, ergoloid, cabazitaxel, imatinib, alectinib, simeprevir, azelastine, plerixafor, dasabuvir, paclitaxel, clofazimine, rupatadine, pyronaridine, sulforidazine, thioproperazine, desmethylazelastine 73  
6LU7 FDA-approved and SWEETLEAD DOCK 6 AMBER 50 ns  Indinavir, ceftin, ivermectin, ceftiofur, cefazedone, amprenavir, neomycin, ceftazidime, ceftizoxime 74  
6LU7 1 million ZINC AutoDock Vina GROMACS 100 ns  Nilotinib, naldemedine, enasidenib, afatinib, ertapenem, ebselen, disulfiram, carmofur, cinanserin, shikonin, tideglusib 75  
6LU7 FDA-approved covalent binders Covdock GROMACS 50 ns  Saquinavir, ritonavir, oseltamivir remdesivir, delavirdine, cefuroxime, prevacid 76  
6LU7 124 antimicrobial drugs Cdocker GROMACS 100 ns  Viomycin, bacampicillin 77  
6Y2E 124 antivirals and protease inhibitors AutoDock Vina    Edoxaban, apixaban, betrixaban 78  
6Y2F 8000 approved and experimental drugs Glide    Fosamprenavir, darunavir, amprenavir, tipranavir, nicotinamide adenine dinucleotide (NAD) 79  
a

Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.

b

Fluvastatin is tested for SARS-COV-2 inhibition.28 

c

Disulfiram is tested for inhibition of Mpro of SARS-CoV-2.30 

At present there are 172 structures available in the PDB for Mpro and hence there is significant choice available for the researchers to use in the docking experiments. Overall, only 14 PDB structures (8% of those available) have been utilized by various research groups. However, from Table 1.5 it can be inferred that most of the research groups (∼62%) have used structure PDB ID 6LU7 (resolution of 2.16 Å) for docking and/or MD studies. This may be due to the availability of this structure in early February 2020 and that it is a co-crystallized structure with a covalent inhibitor N3 bound to CYS145. It is interesting to note that although another Mpro structure, PDB ID 7BQY complexed with N3 at better resolution (1.7 Å) was available, no in silico study utilized this structure, probably due to its later release (22 April 2020) in the Research Collaboratory for Structural Bioinformatics (RCSB) database. The other structures used in four studies each were PDB ID 6W63 (co-crystallized structure with non-covalent inhibitor X77, resolution 2.1 Å) and PDB ID 6Y2F (co-crystallized structure with α-ketoamide inhibitor, resolution 1.95 Å). The PDB structures 6M03 and 6Y2G were used in two studies each and the remainder were used in one study each. Only three studies have used multiple PDB structures for docking to understand the effect of receptor flexibility,32,37,40  while some studies have used multiple structures obtained from MD simulation of the apo Mpro structure.

Two studies have reported homology models for Mpro using different template structures.43,48  In a study by Jiménez-Alberto et al.,43  the Modeller program and several templates were used to generate various homology models and the best model was selected on the basis of Discrete Optimized Protein Energy score. The local structural quality was estimated using the QMEAN tool available in the SWISS-MODEL server. Since the crystal structures of Mpro became available soon after the model was developed, it was compared with 6LU7, whose b-factors agree with the developed model; therefore, the homology model constructed in this work seems to be valid. In contrast, Chen et al.48  performed homology modelling of the apo-enzyme structure of SARS-CoV-2 using SARS-CoV 3CLpro (PDB ID: 2DUC) as the template structure. The model post-energy minization was compared with the obtained crystal structure (PDB ID: 6LU7), having average root mean square deviation (RMSD) of Cα atomic positions (residues 1–300) is 1.2 Å for the A chain and 0.8 Å for the B chain. These RMSD values are acceptable for further structure-based studies using this model.

The docking programs or methods used for screening of drug/ligand databases in Mpro structure are provided in Table 1.6.

The two most used docking software programs were AutoDock Vina and Glide, which were reported in 27% and 23% of the studies on Mpro. Eight studies (15%) reported results from multiple docking programs, whereas the AutoDock program was reported by 12% of studies. There is no single method that has shown excellent retrieval of repurposing opportunities when hits are analyzed based on available experimental results.

Half of the reported studies used large drug database for screening of approved drugs, e.g. DrugBank, SelleckChem, ZINC, etc. The other studies screened smaller sets of drugs classified by different categories such as antivirals (23% of studies), antimalarials (6% of studies) and protease inhibitors (10% of studies).

Only 45% of studies have used MD for Mpro apo structure or for understanding the stability of drug–receptor complexes. In these studies, the most used MD programs were GROMACS (http://www.gromacs.org/, 32% of studies) and AMBER (https://ambermd.org/, 24% of studies) followed by Desmond (https://www.schrodinger.com/desmond) and YASARA (http://www.yasara.org/), each reported in 16% of studies. The NAMD program (http://www.ks.uiuc.edu/Research/namd/) has been reported in three studies (12%).

The MD simulation was performed for various time durations from 1 ns to 200 ns. Most studies (52%) reported a duration of 100 ns for MD simulation and 72% of studies reported a simulation time >50 ns. Thus, most studies have evaluated stability of receptors and drug–receptor complexes after evolving the system for a significant simulation time.

The free energies for the drug–receptor complexes were estimated in 60% of studies that performed MD simulations. The studies have used either molecular mechanics (MM)-generalized Born surface area (GBSA) or MM-Poisson–Boltzmann surface area (PBSA) methods for estimation of free energy. The free-energy estimates were made from the last few conformations of the simulation or from conformations from the last few nanoseconds of the simulation.

From Table 1.5 it can be observed that several antivirals have appeared as hits in different studies: ritonavir (eight studies), saquinavir (seven studies), lopinavir (seven studies), nelfinavir (six studies) and simeprevir (four studies). The other drugs that are reported as hits in multiple studies are disulfiram, apixaban and cobicistat (three studies each). Furthermore, there are 11 studies which have reported screening in PDB ID 6LU7 using the AutoDock Vina program. Overall, 63 drugs have been identified as hits from these studies. Out of these studies, only disulfiram, nelfinavir, saquinavir and tolvaptan appear as promising in two studies. Thus, not many common hits were found in the studies using the combination of 6LU7 and AutoDock Vina. Furthermore, there are six studies that report hits from screening in PDB ID 6LU7 using the Glide program. From these studies, only saquinavir appears as a hit in two studies. Conversely, with docking by Glide in PDB ID 6W63, nelfinavir appears as a hit in two studies. The possible reason for not observing common hits could be due to the different databases screened in these studies as well as some differences in the preparation of proteins and ligands, grid used, centre and other parameters chosen.

This section lists some selected studies that have reported:

  1. correlation with experimental studies noteworthy for some drugs;

  2. use of additional methods for screening of drug-like shape or pharmacophore-based methods;

  3. novel methods used for screening;

  4. important observations from the authors.

Huynh et al. performed MD simulation of apo and ligand-bound Mpro structures31  for preparing targets for further studies. Docking of 19 drugs that were tested in vitro or in clinics for COVID-19 were carried out in target structures. They concluded that a drug molecule can be more potent with a hydrophobic bulky group occupying the “anchor” site in Mpro. Such insights provided by modelling studies are useful for experimentalists, especially those intending to design new drugs against Mpro. We found that five of these drugs were tested in a recombinant assay of Mpro and showed good correlation of Mpro IC50 with binding affinity from docking (r = 0.72). We also observed that six drugs for which cellular in vitro SARS-CoV-2 (EC50)/IC50 values are available show excellent correlation with the binding affinity of drugs calculated in this paper (r = 0.92).

Arun et al. generated an energy-optimized pharmacophore hypothesis using a non-covalent inhibitor X77-bound crystal structure of Mpro of SARS-CoV-2 (PDB ID: 6W63).44  This hypothesis was used for virtual screening followed by screening of selected drugs using the Glide docking program in stepwise manner leading to a shortlist of six drugs. The stability of binding of the selected drugs complexed with Mpro was confirmed by MD simulation to show that binifibrate and bamifylline may bind to the SARS-CoV-2 Mpro active site and inhibit its activity. Kanhed et al.66  also reported use of a pharmacophore model for screening of drugs. This was followed by docking carried out at various precision levels that led to three promising drugs – ritonavir, nelfinavir and saquinavir – that are proposed to be SARS-CoV-2 Mpro inhibitors. Apart from these, pralmorelin, iodixanol and iotrolan were also identified from the systematic screening. The experimental inhibitory assay for Mpro of SARS-CoV-2 showed that nelfinavir and saquinavir have IC50 values of 234 µM and 411 µM, respectively. The corresponding cellular inhibitory activity in SARS-CoV-2 infection showed EC50 values of 1.13 µM and 8.83 µM for nelfinavir and saquinavir, respectively. This clearly indicates that Mpro may not be the primary target for these drugs.

Few studies have utilized ligand shape-based methods for virtual screening of drug databases. Fischer et al. reported use of shape-based screening and ensemble-based docking of a large database of compounds.46  The top-ranked docking poses were subjected to MD simulation, and free-energy estimates using the MM-GBSA method identified apixaban, and the two known antivirals nelfinavir and glecaprevir, as well as rivaroxaban, betrixaban and amprenavir as promising hits. The inhibitory activity against SARS-CoV-2 of some of these drugs are known, e.g. apixaban (5.91 µM), nelfinavir (1.13 µM) and amprenavir (31.32 µM). Ferraz et al. reported use of multiple methods to arrive at drug repurposing.56  They performed a 3D shape-based (using the ROCS program) and electrostatic profile based (using the EON program) similarity search of SARS-CoV-2 Mpro using the X77 ligand (PDB ID: 6W63) as the query ligand. In addition, they generated a structure-based pharmacophore model for drug screening followed by docking to propose glibenclamide, bedaquiline and miconazole as promising hits likely to inhibit SARS-CoV-2 Mpro.

Sencanski et al. reported a novel two-step approach to identify drug repurposing opportunities in Mpro of SARS-CoV-2.55  In the first step, the Informational Spectrum Method Applied for Small Molecules (ISM-SM) was used for searching the DrugBank database to identify drugs having potential interactions with Mpro. In the second step, molecular docking was used as an additional filter to propose repurposed drugs. Using the ISM-SM method, one can determine whether a protein interacts with small molecules and the corresponding binding region in the protein. Regions of allosteric binding are also identified using this method. The promising candidates binding to catalytic sites were mezlocillin, camazepam and spirapril, whereas raltegravir, rolitetracycline, tolvaptan, ciclesonide and rescinnamine were found to target the allosteric domain. Jordaan et al. reported use of density-functional parameters such as chemical potential and hardness in addition to standard virtual screening methods of docking and MD simulation.59  The compounds proposed for drug repurposing from this method include simvastatin, lovastatin, oxacillin, podophyllotoxin and gefitinib.59  Such novel approaches can lead to different list of repurposing candidates missed by conventional methods.

Jin et al. reported identification of lead compounds by combining structure-assisted drug design, virtual drug screening and experimental high-throughput screening.53  The docking studies show that cinanserin fits in the substrate binding pocket of Mpro and has demonstrated activity at 125 µM in Mpro assay. Molecular docking could predict how disulfiram, tideglusib and shikonin bind to Mpro in the substrate-binding pocket. The prediction was confirmed from fluorescence resonance energy transfer assay for disulfiram with EC50 9.35 µM. Gimeno et al. performed virtual screening for Mpro by three different docking programs to identify high-affinity drugs.57  The drugs that have equivalent high-affinity docked poses in all docking schemes were considered as hits. However, this study reported that the docking protocol could not reproduce the experimental results of 26 Mpro inhibitors. The authors considered that the most reliable part of protein–ligand docking algorithms is their capacity to explore the hypothetical binding modes of a ligand at the binding site, and hence proposed visually inspection of the matching of the poses without considering their docking scores. Two of the suggested drugs (celecoxib and carprofen) were tested for Mpro inhibition, but have poor inhibitory activity at 50 µM. Thus, the consensus docking was not found to be an effective tool for virtual screening.

Few structure-based studies are reported for SARS-CoV-2 RNA-dependent RNA polymerase (RdRp), although X-ray crystal structures for this target are available. A summary of the studies conducted so far is given in Table 1.7.

Table 1.7

Summary of RNA-dependent RNA polymerase studiesa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
HM (6NUR) Antivirals AutoDock Vina    Galidesivir, remdesivir, tenofovir, sofosbuvir, ribavirinc 80  
HM (6NUR) 30 known drugs AutoDock Vina NAMD 51 ns  Sofosbuvir, ribavirin, galidesivir, remdesivir, favipiravir, cefuroxime, tenofovir, hydroxychloroquine 82  
HM (6NUR) + 6M71 FDA-approved drugs from e-Drugs3d AutoDock Vina NAMD 100 ns  Quinupristin, dactinomycin, sirolimus, cetrorelix, rifampin 83  
6M71 7922 FDA-approved drugs database from NCGC Glide    RdRp alone: ornipressin, atosiban, lanreotide, argiprestocin, demoxytocin, carbetocin, lypressin, examorelin, colistin, polymyxin B1. RdRp cofactor complex: nacartocin, cistinexine, cisatracurium, pegamotecan, polymyxin B1, ebiratide, sulfomyxine, diagastrin, ditercalinium chloride, benzquercin, examorelin, lypressin, ornipressin, colistin 84  
HM (6NUR) FDA-approved drugs database from SelleckChem Glide Desmond 50 ns MM-GBSA Methylcobalamin 85  
7BW4 6NUR ZINC: FDA-approved, world-not-FDA and investigational-only AutoDock Vina AMBER 5 ns MM-GBSA NSP12-NSP7 interface: nilotinib, dihydroergotamine, pranlukast, cepharanthine, nafamostat. NSP12-NSP8 interface: cepharanthine, dutasteride, simeprevir, grazoprevir, saquinavir 86  
HM (6NUR) 10 antivirals MOE    Remdesivir, sofosbuvir, galidesivir 87  
6M71 30 drugs Molegro    Chlorhexidine, remdesivir, novobiocin, ceftibuten, ribavirin 88  
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
HM (6NUR) Antivirals AutoDock Vina    Galidesivir, remdesivir, tenofovir, sofosbuvir, ribavirinc 80  
HM (6NUR) 30 known drugs AutoDock Vina NAMD 51 ns  Sofosbuvir, ribavirin, galidesivir, remdesivir, favipiravir, cefuroxime, tenofovir, hydroxychloroquine 82  
HM (6NUR) + 6M71 FDA-approved drugs from e-Drugs3d AutoDock Vina NAMD 100 ns  Quinupristin, dactinomycin, sirolimus, cetrorelix, rifampin 83  
6M71 7922 FDA-approved drugs database from NCGC Glide    RdRp alone: ornipressin, atosiban, lanreotide, argiprestocin, demoxytocin, carbetocin, lypressin, examorelin, colistin, polymyxin B1. RdRp cofactor complex: nacartocin, cistinexine, cisatracurium, pegamotecan, polymyxin B1, ebiratide, sulfomyxine, diagastrin, ditercalinium chloride, benzquercin, examorelin, lypressin, ornipressin, colistin 84  
HM (6NUR) FDA-approved drugs database from SelleckChem Glide Desmond 50 ns MM-GBSA Methylcobalamin 85  
7BW4 6NUR ZINC: FDA-approved, world-not-FDA and investigational-only AutoDock Vina AMBER 5 ns MM-GBSA NSP12-NSP7 interface: nilotinib, dihydroergotamine, pranlukast, cepharanthine, nafamostat. NSP12-NSP8 interface: cepharanthine, dutasteride, simeprevir, grazoprevir, saquinavir 86  
HM (6NUR) 10 antivirals MOE    Remdesivir, sofosbuvir, galidesivir 87  
6M71 30 drugs Molegro    Chlorhexidine, remdesivir, novobiocin, ceftibuten, ribavirin 88  
a

Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

c

Remdesivir, tenofovir, sofosbuvir and ribavirin have been tested against SARS-CoV-2.81 

Most of the studies have developed homology models for RdRp using a template structure of SARS-CoV RdRp. In two studies, some key residues were kept as flexible to accommodate changes in the active site.80,83  One study reported virtual screening of drugs in the NSP12 proteins of both SARS-CoV and SARS-CoV-2.86  The published crystal structures for SARS‐CoV‐2 NSP12 (PDB ID: 7BW4) and SARS‐CoV NSP12 (PDB ID: 6NUR) were used as the target proteins. This study showed that binding olysio and cepharanthine were promising in SARS-CoV interfaces, indicating that these drugs could be broad-spectrum antivirals. Among the suggested promising drugs, cepharanthine has been tested for inhibition of SARS-CoV-2 infection. One study uses binding-energy comparison with native nucleotides as a criterion for shortlisting the drugs.80  As can be seen in Table 1.7, four different studies reported remdesivir as a promising hit for RdRp, whereas sofosbuvir appears as a hit in three studies and favipiravir in one study. These drugs are known RdRp inhibitors and have been reported to inhibit SARS-CoV-2 in cellular assays.

Although this protease is an important drug target, very few in silico and experimental studies on drug repurposing using this target are reported in the literature. A recent experimental study demonstrated that none of the FDA-approved drugs show promising binding to SARS-CoV-2 papain-like protease (PLpro).89  Two studies have reported in silico screening in this target.90,91  A summary of the studies conducted so far is given in Table 1.8.

Table 1.8

Summary of papain-like protease protein studiesa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6W9C SelleckChem Glide AMBER 50 ns MM-GBSA Ritonavir, quercetin, phenformin 90  
HM (4MM3) 5 protease inhibitors AutoDock    Nelfinavir, lopinavir, ritonavir, remdesivir, ketoamide 91  
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6W9C SelleckChem Glide AMBER 50 ns MM-GBSA Ritonavir, quercetin, phenformin 90  
HM (4MM3) 5 protease inhibitors AutoDock    Nelfinavir, lopinavir, ritonavir, remdesivir, ketoamide 91  
a

Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

Kandeel et al.89  reported virtual screening of FDA-approved drugs where ritonavir, quercetin and phenformin showed promising binding affinities toward the PLpro target of SARS-CoV-2. As seen earlier, none of the FDA-approved drugs bind effectively the to SARS-CoV-2 PLpro target, hence these predicted drugs may be false positives in the method used for prediction, or the method is inadequate for the purpose. Similarly, from study by Mothay and Ramesh,91  nelfinavir was found to be most active, based on docking score, which may also be a false positive.

Two structure-based drug repurposing studies have reported on the SARS-CoV-2 N-protein92,93  using X-ray crystal structure (PDB ID: 6VYO). A summary of the studies conducted so far is given in Table 1.9.

Table 1.9

Summary of nucleocapsid protein studiesa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6VYO Chloroquine, hydroxychloroquine AutoDock    Chloroquine, hydroxychloroquine 92  
6VYO Antivirals, FDA-approved anti-infectives Glide  50 ns MM-GBSA Zidovudine, valganciclovir, ribavirin 93  
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6VYO Chloroquine, hydroxychloroquine AutoDock    Chloroquine, hydroxychloroquine 92  
6VYO Antivirals, FDA-approved anti-infectives Glide  50 ns MM-GBSA Zidovudine, valganciclovir, ribavirin 93  
a

Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

Amin and Abbas reported docking studies92  of chloroquine and hydroxychloroquine in the SARS-CoV-2 NTD of N-protein and the trend of their binding energies matches with the EC50 of cellular SARS-CoV-2. Yadav et al.93  explored three sites in N-protein for docking. The binding free energies by the MM-GBSA method showed drugs such as zidovudine, valganciclovir and ribavirin with promising binding to all three sites in the N-protein. These drugs have not yet been tested for inhibition of N-protein, but ribavirin has been tested in cellular SARS-CoV-2 infection with an EC50 value of 109.5 µM.

Few structure-based studies are reported for SARS-CoV-2 S-protein, although X-ray crystal structures for this target are available. A summary of these studies is given in Table 1.10.

Table 1.10

Summary of spike protein studiesa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6VSB Ligands, approved drugs from SWEETLEAD database AutoDock Vina GROMACS 18 ns MM-PBSA Quinupristin, nilotinib, tirilazad, selamectin, doramectin 94  
6VSB Nelfinavir AutoDock    Nelfinavir 95  
6LZG DrugBank AutoDock Vina AMBER 100 ns MM-PBSA Digitoxin, nilotinib, lemborexant, raltegravir, antrafenine, flunitrazepam, entrectinib, pazopanib, loxapine 96  
HM (6ACD) 4015 drugs from DrugCentral database Glide GROMACS 100 ns  Streptomycin, ciprofloxacin, glycyrrhizic acid 97  
HM (2AJF) FDA-approved drugs from CureFFI data, DrugCentral and BindingDB AutoDock Vina    Pemirolast, sulfamethoxazole, valaciclovir, sulfanilamide, tazobactam, nitrofurantoin 98  
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6VSB Ligands, approved drugs from SWEETLEAD database AutoDock Vina GROMACS 18 ns MM-PBSA Quinupristin, nilotinib, tirilazad, selamectin, doramectin 94  
6VSB Nelfinavir AutoDock    Nelfinavir 95  
6LZG DrugBank AutoDock Vina AMBER 100 ns MM-PBSA Digitoxin, nilotinib, lemborexant, raltegravir, antrafenine, flunitrazepam, entrectinib, pazopanib, loxapine 96  
HM (6ACD) 4015 drugs from DrugCentral database Glide GROMACS 100 ns  Streptomycin, ciprofloxacin, glycyrrhizic acid 97  
HM (2AJF) FDA-approved drugs from CureFFI data, DrugCentral and BindingDB AutoDock Vina    Pemirolast, sulfamethoxazole, valaciclovir, sulfanilamide, tazobactam, nitrofurantoin 98  
a

Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

None of the drugs proposed as spike glycoprotein inhibitors in the studies listed in Table 1.10 have been validated experimentally against the target. The docking of the nelfinavir mesylate to the spike protein of SARS-CoV‐2 showed that nelfinavir stabilizes near the helices of the HR1 region.95  The pocket between the helices of the fusion peptide and HR1 region as well as the lower part of the NTD region were preferred for binding by nelfinavir over the S1/S2 cleavage site. Batra et al. investigated the spike protein and the spike–ACE-2 interface using machine learning and docking methods.98  The machine learning model uses the Vina score derived from intermolecular contributions of the free energy of binding which ranks molecular conformations. Vina scores of a molecule with the S-protein and S-protein:ACE-2 complex were used for training the models. Based on the chosen screening criteria, 187 ligands were selected from the three datasets, of which 80 were approved drugs. Ensemble docking studies on the selected 187 ligands were performed. Based on this approach, the top approved drugs were proposed for repurposing (Table 1.10). None of these drugs have been tested in an assay of spike protein or in a cellular assay of SARS-CoV-2 infection.

Only two in silico studies reported virtual screening in NSP1.99,100  Although X-ray crystal structures for this target (NSP1) are available, the drug repurposing efforts using structure-based methods reported screening in the homology models of this protein.

Sharma et al.99  developed a homology model of the SARS-CoV-2 NSP1 using structure of NSP1 of SARS-CoV (PDB ID: 2HSX) as the template which covers 68% query protein with 86% identity. Menezes and Silva100  used different template (PDB ID: 2GDT). They performed blind docking of two cyclophilin inhibitors, viz. alisporivir and cyclosporine (known CoV suppressors) as reference standards for further screening in the DrugBank database. The use of reference standards provides the basis for the selection of hits. From the hits of a virtual screening exercise (Table 1.11) none of the drugs have been tested experimentally for inhibition of NSP1.

Table 1.11

Summary of NSP1 studiesa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
HM (2HSX) DrugBank Glide Desmond 20 ns MM-GBSA Esculin, cidofovir, lactose, edoxudine, brivudine, remdesivir 99  
HM (2GDT) DrugBank AutoDock Vina GROMACS 150 ns MM-GBSA Tirilazad, phthalocyanine, Zk-806450 100  
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
HM (2HSX) DrugBank Glide Desmond 20 ns MM-GBSA Esculin, cidofovir, lactose, edoxudine, brivudine, remdesivir 99  
HM (2GDT) DrugBank AutoDock Vina GROMACS 150 ns MM-GBSA Tirilazad, phthalocyanine, Zk-806450 100  
a

Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

Despite having two crystal structures in protein databank, Borgio et al. screened approved drugs in the homology model structure of NSP13/helicase of SARS-CoV-2.101  The SARS-CoV-2 helicase was modelled using template structures of helicase of SARS-CoV (PDB Id:6JYT) and Middle East respiratory syndrome-CoV (PDB ID:5WWP) using the SWISS-MODEL server. The 23 approved antiviral drugs were subjected to flexible molecular docking of drugs in helicase using Molecular Operating Environment (MOE) software. The docking studies showed that vapreotide and atazanavir were the most potent inhibitors of helicase of SARS-CoV-2. None of the drugs have been studied in the assay of helicase of SARS-CoV-2.

The two studies involving drug repurposing using structure-based methods used crystal structures for screening in this target.102,103  Krishnan et al.102  screened a database of 3978 antiviral molecules including approved drugs from the Enamine database (https://enamine.net/). None of the promising hits from this study were approved drugs. Chandra et al.103  used a bound citrate molecule as a reference standard and performed molecular docking to show that idarubicin and glisoxepide were promising hits. Both the drugs have not yet been tested experimentally by any group for inhibition of NSP15 or cellular assay of SARS-CoV-2 infection (Table 1.12).

Table 1.12

Summary of NSP15 studies

HMa(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6VWW 3978 antivirals from Enamine database Glide    No approved drugs as hits 102  
6W01 FDA-approved drugs from ZINC12 AutoDock Vina, iDock, Smina GROMACS 100 ns MM-PBSA Dihydroergotamine, glisoxepide, idarubicin, ergotamine, tasosartan 103  
HMa(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6VWW 3978 antivirals from Enamine database Glide    No approved drugs as hits 102  
6W01 FDA-approved drugs from ZINC12 AutoDock Vina, iDock, Smina GROMACS 100 ns MM-PBSA Dihydroergotamine, glisoxepide, idarubicin, ergotamine, tasosartan 103  
a

Homology model using template with PDB ID.

Drug repurposing efforts using structure-based methods reported screening in the X-ray crystal structures as well as a homology model for this target (NSP16). A summary of these studies is given in Table 1.13.

Table 1.13

Summary of NSP16 studiesa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6W4H Swiss Similarity, DrugBank AutoDock Vina, AutoDock, SWISS-DOCK GROMACS 60 ns  Raltegravir, maraviroc, favipiravir, prednisolone 104  
6W4H FDA drugs from ZINC, DrugBank, Specs database Glide AMBER 200 ns MM-PBSA Fludarabine, saquinavir, cangrelor 105  
HM (3R24, 2XYR, 5YN5) FDA drugs from ZINC, DrugBank AutoDock Vina, AutoDock GROMACS 50 ns MM-GBSA Sinefungin, dihydroergotamine, digitoxin, irinotecan, teniposide 106  
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
6W4H Swiss Similarity, DrugBank AutoDock Vina, AutoDock, SWISS-DOCK GROMACS 60 ns  Raltegravir, maraviroc, favipiravir, prednisolone 104  
6W4H FDA drugs from ZINC, DrugBank, Specs database Glide AMBER 200 ns MM-PBSA Fludarabine, saquinavir, cangrelor 105  
HM (3R24, 2XYR, 5YN5) FDA drugs from ZINC, DrugBank AutoDock Vina, AutoDock GROMACS 50 ns MM-GBSA Sinefungin, dihydroergotamine, digitoxin, irinotecan, teniposide 106  
a

Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

Tazikeh-Lemeski et al.104  used shape-based screening of FDA-approved drugs with a known template of the MTase inhibitor, sinefungin. The selected drugs were further investigated using multiple docking methods followed by MD simulations. The results showed that raltegravir and maraviroc among other compounds can bind strongly to the active site of the protein compared to sinefungin (reference drug) and can be potential candidates to inhibit NSP16. Vijayan et al.105  reported fludarabine, saquinavir and cangrelor as promising drugs. However, these drugs showed poor binding to NSP16 compared to the native ligand (S-adenosyl methionine). This indicates that none of the drugs can act as inhibitors of NSP16. Sharma et al.106  showed that complexes of digitoxin, dihydroergotamine, irinotecan, and teniposide were stable and exhibit more affinity to the receptor compared to the reference ligand, i.e. sinefungin, with IC50 of 286 nM for NSP16 of SARS-CoV-2. The IC50 for the proposed drugs have not been reported for this target in the literature. However, an EC50 for digitoxin of 0.16/0.23 µM has been reported for SARS-CoV-2 infection in cells.

This section reviews four in silico drug repurposing studies involving both main protease and spike glycoprotein using crystal structures as well as the homology models listed in Table 1.14.

Table 1.14

Summary of combined main protease and spike glycoprotein studiesa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
Main protease: 6LU7 2454 FDA-approved drugs from BindingDB AutoDock Vina    Main protease: Saquinavir, tadalafil, rivaroxaban, sildenafil, dasatinib, vardenafil, montelukast 107  
Spike glycoprotein: HM (6ACD) Spike glycoprotein: Ergotamine, amphotericin B, vancomycin, zafirlukast, lanicor 
Main protease: 6Y84 15 antimalarial drugs, 2413 FDA-approved drugs DrugBank Glide NAMD 50 ns MM-PBSA Main protease: Acarbose, colistin, paromomycin 108  
Spike glycoprotein: 6VW1 Spike glycoprotein: Framycetin, acarbose, paromomycin 
Main protease: 6LU7 FDA-approved drugs DrugBank Glide    Main protease: Zanamivir, bortezomib, saquinavir, flavin adenine dinucleotide (FAD) adeflavin, cangrelor, carfilzomib, indinavir, remdesivir 109  
Spike glycoprotein: HM (2GHV) Spike glycoprotein: Cangrelor, flavin adenine dinucleotide (FAD) adeflavin, tiludronate 
Main protease: 6LU7 13 approved antimalarial drugs Glide    Main protease: Halofantrine, mefloquine 110  
Spike glycoprotein: 6M0J, 6YLA Spike glycoprotein: Doxycycline, hydroxychloroquine 
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
Main protease: 6LU7 2454 FDA-approved drugs from BindingDB AutoDock Vina    Main protease: Saquinavir, tadalafil, rivaroxaban, sildenafil, dasatinib, vardenafil, montelukast 107  
Spike glycoprotein: HM (6ACD) Spike glycoprotein: Ergotamine, amphotericin B, vancomycin, zafirlukast, lanicor 
Main protease: 6Y84 15 antimalarial drugs, 2413 FDA-approved drugs DrugBank Glide NAMD 50 ns MM-PBSA Main protease: Acarbose, colistin, paromomycin 108  
Spike glycoprotein: 6VW1 Spike glycoprotein: Framycetin, acarbose, paromomycin 
Main protease: 6LU7 FDA-approved drugs DrugBank Glide    Main protease: Zanamivir, bortezomib, saquinavir, flavin adenine dinucleotide (FAD) adeflavin, cangrelor, carfilzomib, indinavir, remdesivir 109  
Spike glycoprotein: HM (2GHV) Spike glycoprotein: Cangrelor, flavin adenine dinucleotide (FAD) adeflavin, tiludronate 
Main protease: 6LU7 13 approved antimalarial drugs Glide    Main protease: Halofantrine, mefloquine 110  
Spike glycoprotein: 6M0J, 6YLA Spike glycoprotein: Doxycycline, hydroxychloroquine 
a

Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

The homology model generated by Qiao et al.107  compared well with the crystal structure of S-protein released later (e.g. PDB ID: 6LZG, 6VW1). However, such comparison with existing X-ray structure was not provided by Hall and Ji.109  Most of the studies have used Glide for molecular docking. Two studies showed saquinavir as a promising hit for main protease, whereas montelukast was reproduced as Mpro inhibitor of SARS-CoV-2 in one of the studies. Sachdeva et al.110  performed virtual screening in both the targets of the SARS-CoV-2 considering remdesivir as the reference drug. In addition, for docking with SARS-CoV-2 Mpro, the co-crystallized ligand N3 has also been considered as reference. However, it should be noted that the target for remdesivir is RdRp and not Mpro or spike protein, and hence is not a relevant reference drug for these targets.

This section reports in silico drug repurposing studies in multiple structural proteins such as E-protein, N-protein and M-protein using crystal structures as well as homology models111,112  (Table 1.15).

Table 1.15

Summary of structural protein studiesa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
E-protein: HM (5X29) Ligands of E-protein and N-protein from PDB MOE    E-protein: Mycophenolic acid 111  
N-protein: HM (1SSK) 
E-protein: HM (2MM4) 200 natural and 348 antiviral compounds from SelleckChem AutoDock Vina, AutoDock GROMACS 100 ns  E-protein: Rutin, doxycycline 112  
N-protein:6-3M N-protein:Simeprevir, grazoprevir 
M-protein:HM (4F91) M-protein:Caffeic acid, ferulic acid 
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
E-protein: HM (5X29) Ligands of E-protein and N-protein from PDB MOE    E-protein: Mycophenolic acid 111  
N-protein: HM (1SSK) 
E-protein: HM (2MM4) 200 natural and 348 antiviral compounds from SelleckChem AutoDock Vina, AutoDock GROMACS 100 ns  E-protein: Rutin, doxycycline 112  
N-protein:6-3M N-protein:Simeprevir, grazoprevir 
M-protein:HM (4F91) M-protein:Caffeic acid, ferulic acid 
a

Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

Azeez et al. reported the homology model of the envelope and nucleocapsid phosphoprotein of SARS-CoV-2.111  Some selected ligands known to bind E-protein and N-protein were docked using MOE software. A promising drug binding to envelope protein was mycophenolic acid. Mycophenolate mofetil has shown inhibitory activity against SARS-CoV-2 in VeroE6 cells with an EC50 of 0.47 µM. In another study, Bhowmik et al. identified potential molecules against three essential structural proteins of SARS-CoV-2, envelope (E)-protein, membrane (M)-protein and nucleocapsid (N)-protein.112  The proposed hits have not yet been experimentally tested in the assay of respective proteins, but doxycycline has been reported to have EC50 of 5.6 µM for cellular inhibition of SARS-CoV-2.

Two in silico studies reported drug repurposing on proteases, viz. main protease and papain-like protease113,114  (Table 1.16).

Table 1.16

Summary of studies on proteasesa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
Main protease: 6LU7 8 protease inhibitors VINA/VegaZZ    Main protease:Lopinavir, darunavir, famotidine 113  
PLpro: 6WUC PLpro: Ribavirin, famotidine 
Main protease: 6LU7 Databases of natural products, approved drugs from ZINC AutoDock Vina GROMACS 50 ns MM-PBSA Main protease: Lopinavir, tipranavir, nelfinavir 114  
PLpro: 6W9C PLpro: Lopinavir, tipranavir, nelfinavir 
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
Main protease: 6LU7 8 protease inhibitors VINA/VegaZZ    Main protease:Lopinavir, darunavir, famotidine 113  
PLpro: 6WUC PLpro: Ribavirin, famotidine 
Main protease: 6LU7 Databases of natural products, approved drugs from ZINC AutoDock Vina GROMACS 50 ns MM-PBSA Main protease: Lopinavir, tipranavir, nelfinavir 114  
PLpro: 6W9C PLpro: Lopinavir, tipranavir, nelfinavir 
a

Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

From a docking study in Mpro,113  both lopinavir and darunavir showed lower binding free energy as compared to famotidine. A similar trend was found in docking studies in PLpro, where the antiviral drug ribavirin showed lower binding energy than famotidine. Mitra et al. developed two pharmacophore models (Ph-1 and Ph-2) obtained from reported specific inhibitors of SARS-CoV Mpro and PLpro.114  The shortlisted hits from pharmacophore models were docked into the active sites of SARS-CoV-2 Mpro and model of PLpro followed by MD simulations. Both nelfinavir and tipranavir showed stable binding to both proteases indicating promising repurposed candidates. The reported Mpro inhibitory activity for nelfinavir and tipranavir was 234 µM and 180 µM, respectively, whereas it was not reported for PLpro.

There are studies in the literature which report structure-based studies on multiple SARS-CoV-2 targets along with some human targets, and these are summarized in Table 1.17.

Table 1.17

Summary of studies on all key viral and human targetsa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
NSP1: HM (2K87) Databases of natural products, approved drugs from ZINC + 78 antiviral drugs ICM    NSP1:Piperacillin, cefpiramide, streptomycin, lymecycline, tetracycline 115  
NSP3b: HM (2FKV)       NSP3b: Remdesivir, lopinavir, chloroquine  
NSP3c: HM (2W2G)       NSP3c: Ritonavir, darunavir, lopinavir  
PLpro: HM (3E9S)       PLpro: Ribavirin, valganciclovir, thymidine, chloramphenicol, cefamandole, tigecycline, chlorphenesin carbamate, levodropropizine  
Mpro: HM (2AW0)       Mpro: Lymecycline, demeclocycline, doxycycline, oxytetracycline, nicardipine, telmisartan, conivaptan, montelukast  
NSP7_Nsp8 complex: HM (2AHM)       NSP7_Nsp8 complex: Arbidol  
NSP9: HM (3EE7)         
Nsp10: HM (5NFY)         
RdRp: HM (6NUR)       RdRp: Itraconazole, novobiocin, chenodeoxycholic acid, cortisone, idarubicin, silybin, pancuronium bromide, dabigatran etexilate  
Helicase: HM (6JYT)       Helicase: Lymecycline, cefsulodin, rolitetracycline, itraconazole, saquinavir, dabigatran, canrenoic acid  
NSP14: HM (5NFY)       NSP14: Arbidol  
NSP15: HM (2H85)       NSP15: Arbidol  
NSP16: HM (3R24)         
S-protein: HM (3SCI)       S-protein: Rescinnamine, iloprost, prazosin, posaconazole, itraconazole, sulfasalazine, azlocillin, penicillin, cefsulodin, dabigatran etexilate  
TMPRSS2: HM (1Z8A)       TMPRSS2: Pivampicillin, hetacillin, cefoperazone, clindamycin  
ACE-2: Spike–ACE-2: Docked       ACE-2: Troglitazone, losartan, ergotamine, cefmenoxime, silybin  
      Spike–ACE-2: Hesperidine  
NSP1: HM (2GDT)  DrugBank AutoDock Vina    Mpro: Dolutegravir, raltegravir, saquinavir, bictegravir, beclabuvir, tivatinib, filibuvir 116  
NSP3: HM (6VXS)         
NSP4: HM (3VCB)         
Mpro: 6LU7         
NSP7: 6M71         
NSP8: 7BV1        NSP8: Saquinavir 
NSP9: 6W4B         
Nsp10: 6ZCT        Nsp10: Saquinavir 
RdRp: 7BV2        RdRp: Saquinavir, remdesivir, filibuvir, sofosbuvir 
Helicase: HM (6JYT)        Helicase: Saquinavir, bictegravir 
NSP14: HM (5C8S)        NSP14: Saquinavir, bictegravir, nilotinib, montelukast 
NSP15: 6W01        NSP15: Bictegravir, remdesivir, nilotinib 
NSP16: 6W4H        NSP16: Dolutegravir, raltegravir, saquinavir, montelukast 
S-protein: 6VYB        S-protein: Saquinavir 
N-protein: HM (2GIB)        N-protein: Radotinib 
E-protein: HM (1SSK)        E-protein: Beclabuvir 
ACE-2: 6CS2         
NSP3: 6VXS 11 promising drugs AutoDock Vina    NSP3: Lopinavir, pemirolast, eriodictyol 117  
Mpro: 6LU7, 6Y84, 6M03        Mpro: Lopinavir, remdesivir, eriodictyol, ritonavir, pemirolast, ribavirin, mycophenolic acid, chloroquine, hydroxychloroquine 
NSP9: 6W4B        NSP9: Lopinavir, eriodictyol, pemirolast 
NSP10: 6W75        NSP10: Remdesivir, eriodictyol 
NSP16: 6W75        NSP16: Ritonavir, eriodictyol 
S-protein: 6VSB, 2GHV        S-protein: Remdesivir, lopinavir, ritonavir, eriodictyol 
ACE-2: 6M18        ACE-2: Lopinavir, ritonavir 
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
NSP1: HM (2K87) Databases of natural products, approved drugs from ZINC + 78 antiviral drugs ICM    NSP1:Piperacillin, cefpiramide, streptomycin, lymecycline, tetracycline 115  
NSP3b: HM (2FKV)       NSP3b: Remdesivir, lopinavir, chloroquine  
NSP3c: HM (2W2G)       NSP3c: Ritonavir, darunavir, lopinavir  
PLpro: HM (3E9S)       PLpro: Ribavirin, valganciclovir, thymidine, chloramphenicol, cefamandole, tigecycline, chlorphenesin carbamate, levodropropizine  
Mpro: HM (2AW0)       Mpro: Lymecycline, demeclocycline, doxycycline, oxytetracycline, nicardipine, telmisartan, conivaptan, montelukast  
NSP7_Nsp8 complex: HM (2AHM)       NSP7_Nsp8 complex: Arbidol  
NSP9: HM (3EE7)         
Nsp10: HM (5NFY)         
RdRp: HM (6NUR)       RdRp: Itraconazole, novobiocin, chenodeoxycholic acid, cortisone, idarubicin, silybin, pancuronium bromide, dabigatran etexilate  
Helicase: HM (6JYT)       Helicase: Lymecycline, cefsulodin, rolitetracycline, itraconazole, saquinavir, dabigatran, canrenoic acid  
NSP14: HM (5NFY)       NSP14: Arbidol  
NSP15: HM (2H85)       NSP15: Arbidol  
NSP16: HM (3R24)         
S-protein: HM (3SCI)       S-protein: Rescinnamine, iloprost, prazosin, posaconazole, itraconazole, sulfasalazine, azlocillin, penicillin, cefsulodin, dabigatran etexilate  
TMPRSS2: HM (1Z8A)       TMPRSS2: Pivampicillin, hetacillin, cefoperazone, clindamycin  
ACE-2: Spike–ACE-2: Docked       ACE-2: Troglitazone, losartan, ergotamine, cefmenoxime, silybin  
      Spike–ACE-2: Hesperidine  
NSP1: HM (2GDT)  DrugBank AutoDock Vina    Mpro: Dolutegravir, raltegravir, saquinavir, bictegravir, beclabuvir, tivatinib, filibuvir 116  
NSP3: HM (6VXS)         
NSP4: HM (3VCB)         
Mpro: 6LU7         
NSP7: 6M71         
NSP8: 7BV1        NSP8: Saquinavir 
NSP9: 6W4B         
Nsp10: 6ZCT        Nsp10: Saquinavir 
RdRp: 7BV2        RdRp: Saquinavir, remdesivir, filibuvir, sofosbuvir 
Helicase: HM (6JYT)        Helicase: Saquinavir, bictegravir 
NSP14: HM (5C8S)        NSP14: Saquinavir, bictegravir, nilotinib, montelukast 
NSP15: 6W01        NSP15: Bictegravir, remdesivir, nilotinib 
NSP16: 6W4H        NSP16: Dolutegravir, raltegravir, saquinavir, montelukast 
S-protein: 6VYB        S-protein: Saquinavir 
N-protein: HM (2GIB)        N-protein: Radotinib 
E-protein: HM (1SSK)        E-protein: Beclabuvir 
ACE-2: 6CS2         
NSP3: 6VXS 11 promising drugs AutoDock Vina    NSP3: Lopinavir, pemirolast, eriodictyol 117  
Mpro: 6LU7, 6Y84, 6M03        Mpro: Lopinavir, remdesivir, eriodictyol, ritonavir, pemirolast, ribavirin, mycophenolic acid, chloroquine, hydroxychloroquine 
NSP9: 6W4B        NSP9: Lopinavir, eriodictyol, pemirolast 
NSP10: 6W75        NSP10: Remdesivir, eriodictyol 
NSP16: 6W75        NSP16: Ritonavir, eriodictyol 
S-protein: 6VSB, 2GHV        S-protein: Remdesivir, lopinavir, ritonavir, eriodictyol 
ACE-2: 6M18        ACE-2: Lopinavir, ritonavir 
a

Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

Three studies have reported screening of drugs in several key viral and human targets involved in COVID-19.115–117 

Wu et al. reported one of the earlier computational drug repurposing studies in the early stages of SARS-CoV-2 infection.115  The authors systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses to predict their structures using homology modelling with PDB1018 database. The built model of the SARS-CoV-2 3CLpro compared with its crystal structure of SARS-CoV-2 3CLpro (IDB ID: 6LU7) showed a Cα RMSD of 0.471 Å on the overall structure and 0.126 Å in the substrate binding regions indicating adequate quality of the models for further studies. The selected drug database was screened using docking to identify possible targets of these compounds and potential drugs acting on certain targets, as reported in Table 1.17.

Xu et al. reported large-scale structure-based virtual screening116  in 16 viral proteins and ACE-2 protein (PDB ID: 6CS2). The promising hits from this large-scale exercise are reported (https://shennongproject.ai/) and include the following.

  1. Dolutegravir and raltegravir exhibit strong binding affinity with multiple targets such as the main protease and exonuclease.

  2. Saquinavir shows a strong affinity with the main protease as well as with many targets like S-protein, NSP8, NSP10, NSP12, NSP13, NSP14, and NSP16, suggesting that saquinavir might be a multi-target inhibitor.

  3. Bictegravir exhibited strong affinity with Mpro, NSP13, NSP14, and NSP15, making it one of best performing drugs.

  4. The cancer drugs like tivantinib, lifirafenib, entrectinib, nilotinib and radotinib and asthma drugs like montelukast and zafirlukast also were promising.

  5. It is noted that the binding affinity of remdesivir with RdRp is poor over that with endonuclease, indicating limitations of this docking.

Some of the drugs reported as promising in this study have been investigated experimentally with saquinavir exhibiting an IC50 of 411 µM in Mpro, whereas its cellular assay for SARS-CoV-2 infection showed an EC50 of 8.83 µM, indicating that Mpro may not be the target of saquinavir. Dolutegravir and remdesivir have been reported to have an EC50 of 22.04 µM and 0.77 µM, respectively, for cellular SARS-CoV-2 infection.

Deshpande et al.117  showed that ritonavir exhibited efficient binding to NSP16 and spike protein, whereas remdesivir is more active against Mpro, spike protein and NSP10. Lopinavir showed promising binding with Mpro, NSP9, spike glycoprotein, NSP3 and ACE 2 protein. Eriodictyol showed high binding affinity against all selected proteins and pemirolast showed high binding efficiencies against Mpro and NSP9. Thus, the screened drugs have potential against multiple targets. The available experimental information shows that lopinavir binding to Mpro has been reproduced in this study.

The following studies have reported screening of drugs in main protease as well as some other viral and human targets involved in COVID-19.118–132 

Cavasotto and Filippo performed a docking-based screening of approved drugs118  with receptor flexibility and multiple structures and ranking was calculated using ICM (ICM pro software) and QMDS (QM docking scoring function) scoring leading to identification of promising drugs for various targets as reported in Table 1.18. The Mpro inhibition observed in experiments for saquinavir and indinavir has been reproduced in this study. Iftikhar et al. explored in silico drug repurposing120  using a ranking score based on binding energy, clustering score, shape complementarity and functional significance of the binding pocket was applied to identify promising binders to respective targets. Three targets of SARS-CoV-2, viz. Mpro/3CLpro, RdRp and spike–ACE-2 interface investigated by Alexpandi et al.121  provided hits from quinoline drugs. None of them except saquinavir has been experimentally validated in their respective targets or even for SARS-CoV-2 inhibition. Saquinavir exhibited IC50 of 411 µM in Mpro, whereas its in cellular assay for SARS-CoV-2 infection showed an EC50 of 8.83 µM, indicating that Mpro may not be the target of saquinavir.

Table 1.18

Summary of studies on main protease and other targetsa

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
Mpro: 6YB7, 6LU7 11 552 unique compounds from ChEMBL, DrugBank, DrugCentral, SelleckChem ICM    Mpro: Saquinavir, ritonavir, indinavir 118  
PLpro: 3E9S, 6WX4 PLpro: Ziprasidone, darolutamide 
S-protein: 6M17 S-protein: Pralatrexate 
Mpro: 6LU7 4500 drugs from ChEMBL, DrugBank and SelleckChem AutoDock Vina NAMD 50 ns MM-PBSA Mpro: Talampicillin, lurasidone 119  
TMPRSS2: HM (2OQ5) TMPRSS2: Rubitecan, loprazolam 
Mpro: 6LU7 4512 FDA-approved drugs from MTiOpen screen AutoDock Vina, AutoDock    Mpro: Rimantadine, bagrosin, grazoprevir 120  
RdRp: HM (6NUR) RdRp: Casopitant 
Helicase: HM (6JYT) Helicase: Meclonazepam, oxiphenisatin 
Mpro: 6LU7 113 quinoline-based drugs from DrugBank iGEMDOCK, AutoDock Vina    Mpro: Rilapladib, saquinavir, oxolinic acid, elvitegravir, batefenterol, sitafloxacin, alatrofloxacin, quarfloxin 121  
RdRp: HM (6NUR) RdRp: Elvitegravir, oxolinic acid, saquinavir, garenoxacin, rilapladib, pelitinib, difloxacin, batefenterol, danofloxacin 
Spike–ACE-2 interface: 6M17 Spike–ACE-2 interface: Saquinavir, rilapladib, quarfloxin, batefenterol, oxolinic acid, alatrofloxacin, dovitinib, rebamipide 
Mpro: 6LU7 76 antiviral drugs from Pubchem AutoDock Vina, GOLD YASARA 100 ns MM-PBSA Mpro: Raltegravir, daclatasvir simeprevir, cobicistat, remdesivir 122  
RdRp: HM (6NUR) RdRp: Raltegravir, daclatasvir, simeprevir, cobicistat, remdesivir 
Mpro: 6Y84 24 selected drugs AutoDock Vina    Mpro: Saquinavir, metaquine, mefloquine, nelfinavir, piperaquine, dolutegravir 123  
NSP9: 6W4B NSP9: Saquinavir, metaquine 
NSP15: 6VWW NSP15: Saquinavir, metaquine, mefloquine, lopinavir, ritonavir 
Spike–ACE-2 interface: HM Spike–ACE-2 interface: Saquinavir, metaquine, lopinavir, ritonavir 
Mpro: 6LU7 Database of antiviral, anti-infectious and antiprotease compounds from SelleckChem Glide    Mpro: Cyclocytidine hydrochloride, trifluridine, adonitol, meropenem, penciclovir 125  
NSP10/NSP16: 6W6I NSP10/NSP16: Telbivudine, oxytetracycline dihydrate, methylgallate, 2-deoxyglucose, daphnetin 
Mpro: 6Y84 FDA-approved drugs from ZINC database AutoDock Vina NAMD 20 ns MM-GBSA Mpro: Tetracycline, dihydroergotamine, dutasteride, nelfinavir, paliperidone 126  
RdRp–NSP8 interface: HM (6NUR) RdRp–NSP8 interface: Ergotamine, dihydroergotamine, bromocriptine, tipranavir, conivaptan, eltrombopag 
Mpro: 6LU7 4 known drugs AutoDock Vina    Mpro: Ribavirin, luteolin 127  
S-protein: 6VSB S-protein: Luteolin 
RdRp: HM (6NUS) RdRp: Luteolin 
PLpro: HM (4OVZ) PLpro: Ribavirin, luteolin 
Mpro: 1Q2Wc Anitimalarial, antifungal, antiviral drugs MOE    Mpro: Brincidofovir 128  
ACE-2: 6M0J ACE-2: Brincidofovir 
Mpro: 6LU7 Anti-HIV, anti-hepatitis drugs, antiviral phytochemicals AutoDock    Mpro: Paritaprevir, ergotamine tartrate, telaprevir, dihydroergotamine, simeprevir, ergotamine alkaloid, telmisartan, ritonavir 129  
RdRp: 6NUR RdRp: Pemetrexed, raltitrexed 
PLpro: 3E9S PLpro: Chloroquine, paritaprevir 
Furin: 6HZD Furin: Paritaprevir, chloroquine, ritonavir 
Mpro: HM (1UJ1) 123 antiviral drugs AutoDock Vina GROMACS 100 ns  Mpro: Paritaprevir, raltegravir 130  
NSP16: HM (3R24) NSP16: Dolutegravir, bictegravir 
Mpro: HMd 2471 FDA-approved drugs from DrugBank AutoDock Vina    Mpro: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin 131  
PLpro: HMd PLpro: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin 
S-protein: HMd S-protein: Glycyrrhizic acid, hesperidin 
ACE-2: 1R42 ACE-2: Glycyrrhizic acid, hesperidin 
TMPRSS2: HMd TMPRSS2: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin 
HR1: HMd HR1: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin 
Mpro: 6Y2E 3 drugs AutoDock Vina    Mpro: Azithromycin, chloroquine, hydroxychloroquine 132  
S-protein: 6VW1 S-protein: Azithromycin, chloroquine 
ACE-2: 6VW1 ACE-2: Azithromycin, chloroquine 
Cathepsin: 2XU3 Cathepsin: Azithromycin, chloroquine, hydroxychloroquine 
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
Mpro: 6YB7, 6LU7 11 552 unique compounds from ChEMBL, DrugBank, DrugCentral, SelleckChem ICM    Mpro: Saquinavir, ritonavir, indinavir 118  
PLpro: 3E9S, 6WX4 PLpro: Ziprasidone, darolutamide 
S-protein: 6M17 S-protein: Pralatrexate 
Mpro: 6LU7 4500 drugs from ChEMBL, DrugBank and SelleckChem AutoDock Vina NAMD 50 ns MM-PBSA Mpro: Talampicillin, lurasidone 119  
TMPRSS2: HM (2OQ5) TMPRSS2: Rubitecan, loprazolam 
Mpro: 6LU7 4512 FDA-approved drugs from MTiOpen screen AutoDock Vina, AutoDock    Mpro: Rimantadine, bagrosin, grazoprevir 120  
RdRp: HM (6NUR) RdRp: Casopitant 
Helicase: HM (6JYT) Helicase: Meclonazepam, oxiphenisatin 
Mpro: 6LU7 113 quinoline-based drugs from DrugBank iGEMDOCK, AutoDock Vina    Mpro: Rilapladib, saquinavir, oxolinic acid, elvitegravir, batefenterol, sitafloxacin, alatrofloxacin, quarfloxin 121  
RdRp: HM (6NUR) RdRp: Elvitegravir, oxolinic acid, saquinavir, garenoxacin, rilapladib, pelitinib, difloxacin, batefenterol, danofloxacin 
Spike–ACE-2 interface: 6M17 Spike–ACE-2 interface: Saquinavir, rilapladib, quarfloxin, batefenterol, oxolinic acid, alatrofloxacin, dovitinib, rebamipide 
Mpro: 6LU7 76 antiviral drugs from Pubchem AutoDock Vina, GOLD YASARA 100 ns MM-PBSA Mpro: Raltegravir, daclatasvir simeprevir, cobicistat, remdesivir 122  
RdRp: HM (6NUR) RdRp: Raltegravir, daclatasvir, simeprevir, cobicistat, remdesivir 
Mpro: 6Y84 24 selected drugs AutoDock Vina    Mpro: Saquinavir, metaquine, mefloquine, nelfinavir, piperaquine, dolutegravir 123  
NSP9: 6W4B NSP9: Saquinavir, metaquine 
NSP15: 6VWW NSP15: Saquinavir, metaquine, mefloquine, lopinavir, ritonavir 
Spike–ACE-2 interface: HM Spike–ACE-2 interface: Saquinavir, metaquine, lopinavir, ritonavir 
Mpro: 6LU7 Database of antiviral, anti-infectious and antiprotease compounds from SelleckChem Glide    Mpro: Cyclocytidine hydrochloride, trifluridine, adonitol, meropenem, penciclovir 125  
NSP10/NSP16: 6W6I NSP10/NSP16: Telbivudine, oxytetracycline dihydrate, methylgallate, 2-deoxyglucose, daphnetin 
Mpro: 6Y84 FDA-approved drugs from ZINC database AutoDock Vina NAMD 20 ns MM-GBSA Mpro: Tetracycline, dihydroergotamine, dutasteride, nelfinavir, paliperidone 126  
RdRp–NSP8 interface: HM (6NUR) RdRp–NSP8 interface: Ergotamine, dihydroergotamine, bromocriptine, tipranavir, conivaptan, eltrombopag 
Mpro: 6LU7 4 known drugs AutoDock Vina    Mpro: Ribavirin, luteolin 127  
S-protein: 6VSB S-protein: Luteolin 
RdRp: HM (6NUS) RdRp: Luteolin 
PLpro: HM (4OVZ) PLpro: Ribavirin, luteolin 
Mpro: 1Q2Wc Anitimalarial, antifungal, antiviral drugs MOE    Mpro: Brincidofovir 128  
ACE-2: 6M0J ACE-2: Brincidofovir 
Mpro: 6LU7 Anti-HIV, anti-hepatitis drugs, antiviral phytochemicals AutoDock    Mpro: Paritaprevir, ergotamine tartrate, telaprevir, dihydroergotamine, simeprevir, ergotamine alkaloid, telmisartan, ritonavir 129  
RdRp: 6NUR RdRp: Pemetrexed, raltitrexed 
PLpro: 3E9S PLpro: Chloroquine, paritaprevir 
Furin: 6HZD Furin: Paritaprevir, chloroquine, ritonavir 
Mpro: HM (1UJ1) 123 antiviral drugs AutoDock Vina GROMACS 100 ns  Mpro: Paritaprevir, raltegravir 130  
NSP16: HM (3R24) NSP16: Dolutegravir, bictegravir 
Mpro: HMd 2471 FDA-approved drugs from DrugBank AutoDock Vina    Mpro: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin 131  
PLpro: HMd PLpro: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin 
S-protein: HMd S-protein: Glycyrrhizic acid, hesperidin 
ACE-2: 1R42 ACE-2: Glycyrrhizic acid, hesperidin 
TMPRSS2: HMd TMPRSS2: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin 
HR1: HMd HR1: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin 
Mpro: 6Y2E 3 drugs AutoDock Vina    Mpro: Azithromycin, chloroquine, hydroxychloroquine 132  
S-protein: 6VW1 S-protein: Azithromycin, chloroquine 
ACE-2: 6VW1 ACE-2: Azithromycin, chloroquine 
Cathepsin: 2XU3 Cathepsin: Azithromycin, chloroquine, hydroxychloroquine 
a

Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

c

Wrong choice of target.

d

Information on template used for homology modelling not provided in the reference.

Ahmed et al.122  developed a quantitative structure–activity relationship (QSAR) model using docking energies as dependent variables and several molecular descriptors as independent variables. QSAR analysis shows that molecular weight, Total Polar Surface Area TPSA (Å2), number of hydrogen donor, hydrogen atom, and C–C single bonds contribute to the variation in binding affinity. Such QSAR relationships would help estimating binding affinity of novel molecules designed from similar class of drugs. The study by Barros et al.123  proposed that saquinavir and metaquine exhibit binding to Mpro, NSP9, NSP15 and the spike–ACE-2 interface and may be promising candidates. Saquinavir, nelfinavir and lopinavir have been tested in Mpro assay with IC50 of 411, 234 and 486 µM, respectively.124  The cellular assay for these drugs for SARS-CoV-2 infection showed EC50 of 8.83, 1.13, and 9.12 µM, respectively. This indicates that Mpro may not be the target for these drugs for inhibition of SARS-CoV-2 infection. Yu et al.127  showed that luteolin binds to all studied targets, viz. 3CLpro, PLpro, RdRp and spike glycoprotein. Luteolin exhibited inhibition of the spike–ACE-2 complex in SARS-CoV with an EC50 of 9.02 µM; however, this has not yet been explored experimentally in other target assays.

Manikyam and Joshi129  performed molecular docking in multiple human and SARS-CoV-2 proteins. Among the proposed hits, the drugs tested experimentally in Mpro assay are telaprevir and simeprevir, with IC50 of 10.7 and 13.74 µM, respectively. The other drugs have not been tested in assays of the respective targets. It should be noted that the authors claim to have performed molecular docking in the viral spike protein; however, no structure or model for spike protein was used for screening. Khan et al.130  systematically explored docking of 123 antiviral drugs against 3CLpro and NSP16/2′-OMTase of SARS-CoV-2 with two drugs each were found to be hits for both the targets. Dolutegravir, a predicted NSP16 inhibitor, has shown inhibition of SAR-CoV-2 infection in cellular assay with EC50 of 22.04 µM.

A summary of in silico screening in mixed SARS-CoV-2 and human targets not involving screening in main protease is reported in this section (Table 1.19).

Table 1.19

Summary of studies on targets other than main proteasea

HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
Helicase: HM (6JYT) 54 FDA-approved antiviral drugs AutoDock    Helicase: Simeprevir, paritaprevir, grazoprevir 133  
NSP14: HM (5C8S) NSP14: Simeprevir, paritaprevir, grazoprevir, sinefungin 
S-protein: 6VYB 4 drugs AutoDock GROMACS 50 ns MM-PBSA S-protein: Chloroquine, SAA09E2 134  
RdRp: 6M71 RdRp: Remdesivir, Favipiravir 
ACE-2: 1R4L  
NSP9: 6W4B 2000 FDA-approved drugs AutoDock Vina, AutoDock Desmond 100 ns  NSP9: Conivaptan, telmisartan, phaitanthrin D 135  
S-protein: 6LZG S-protein: Tegobuvir, bromocriptine, baicalin 
HMb(template)/PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
Helicase: HM (6JYT) 54 FDA-approved antiviral drugs AutoDock    Helicase: Simeprevir, paritaprevir, grazoprevir 133  
NSP14: HM (5C8S) NSP14: Simeprevir, paritaprevir, grazoprevir, sinefungin 
S-protein: 6VYB 4 drugs AutoDock GROMACS 50 ns MM-PBSA S-protein: Chloroquine, SAA09E2 134  
RdRp: 6M71 RdRp: Remdesivir, Favipiravir 
ACE-2: 1R4L  
NSP9: 6W4B 2000 FDA-approved drugs AutoDock Vina, AutoDock Desmond 100 ns  NSP9: Conivaptan, telmisartan, phaitanthrin D 135  
S-protein: 6LZG S-protein: Tegobuvir, bromocriptine, baicalin 
a

Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.

b

Homology model using template with PDB ID.

Gurung reported homology modelling of two SARS-CoV-2 targets, viz. NSP13 helicase and NSP14 (N7-MTase domain) for drug repurposing.133  None of the hit drugs have been experimentally tested in the respective assays and cellular assay. Bibi et al.134  explored inhibitory mechanisms of four drugs in multiple targets. The molecular docking and MD simulation indicated that remdesivir and favipiravir block the catalytic site of RdRp, which corroborates experimental findings.

Based on this discussion, it is clear that a large number of in silico studies (∼52) have been performed on main protease as compared to other SARS-CoV-2 proteins. In these studies, protein structures having co-crystallized ligands were preferably selected over apo structures (e.g. for main protease the structure with inhibitor N3 was used; PDB ID: 6LU7 in most reports). In the general procedure for docking that emerges from various studies, the hydrogens are added to the selected protein structure and the system is minimized using the force-field method. The drug structures are taken from the literature and are energy-minimized. The docking of drugs or ligands is done within the binding pocket of co-crystallized ligand (e.g. N3 site for Mpro or respective sites in selected targets) by several studies; however, only a few have explored all cavities in the target. For screening of a library of drugs or ligands, majority of studies have used AutoDock Vina as initial screening methodology. The filtered drugs are further docked with additional precision using AutoDock program. The other programs used for docking are Glide, ICM, iDock, etc. with increasing precision of docking in the respective software. The drug–receptor complexes of promising hits from the docking exercise are normally subjected to MD simulation to confirm the stability of the complex. The initial complex structure taken from docking is subjected to 1 ns to 20 ns MD simulations using different tools like AMBER, GROMACS, NAMD, etc. The filtered hits from such short MD are subjected to production runs of long duration such as 50 ns to 200 ns MD simulations and complexes are evaluated for stability using RMSD, radius of gyration, root mean square fluctuations, intermolecular H-bonds, solvent accessible surface area, etc. The binding energy is generally calculated from the MM-GBSA or MM-PBSA method. Based on comparison of binding energy obtained from such exercises for reference drug/ligand, promising hits for repurposing in SARS-CoV-2 are proposed (hits have lower binding energy than reference drug/ligand). The reference drugs/ligands are known drugs/ligands or co-crystallized ligands for the selected target (e.g. remdesivir has been used as a reference ligand for RdRp, whereas N3 has been used for Mpro/3CLpro). The majority of drugs bind to the active site or co-crystallized ligand site and there are rare reports of allosteric binding of drugs in proteins. For the protein structures without co-crystallized ligands, all cavities have been explored to find best binding site for the drugs. The assessment of promising hits from various structure-based approaches shows that many have potential to provide clues to experimentalists. Some of the structure-based studies have demonstrated that they are able to reproduce experimental information, e.g. remdesivir was a promising hit in RdRp and boceprevir, telaprevir, atazanavir were promising hits in Mpro. One study also demonstrated good correlation between calculated binding affinity or binding scores and Mpro assay as well as SARS-CoV-2 inhibitory activity (IC50/EC50). Thus, it may be inferred that structure-based approaches indeed provide clues for selecting drugs for experimental screening in SARS-CoV-2 infection.

Based on available experimental information on Mpro assay as well as SARS-CoV-2 cellular assay, it may be inferred that Mpro may not be the primary target for following drugs: nelfinavir, saquinavir, lopinavir and tipranavir, although they have predicted as promising hits in several structure-based studies reported earlier.

The ligand-based approaches make use of QSAR, similarity-based approaches and pharmacophore-based approaches for identifying drug repurposing opportunities. These approaches largely depend on available information of ligands with corresponding experimental activities against specific target as collected in different databases such as BindingDB, ChEMBL and PubChem. A limited number of reports have been published that cover ligand-based approaches for drug repurposing in SARS-CoV-2.136–138 

Alves et al.136  reported QSAR using the SARS-CoV Mpro inhibitory activities of 113 compounds, while Gao et al.137  reported a machine-learning ligand-based model using inhibitory activities of 314 SARS-CoV or SARS-CoV-2 3CLpro inhibitors. The developed QSAR models were used to screen larger drug datasets like DrugBank and FDA-approved drugs. These two articles suggest sufugolix, cenicriviroc, proglumetacin, lopinavir, ritonavir, tetrandrine, cobicistat, losartan, ribavirin, remdesivir, aviptadil, danoprevir, proflavine, chloroxine, demexiptiline, fluorouracil and oteracil for repurposing. Alves et al. also performed docking of drugs in Mpro of SARS-CoV-2 (PDB ID: 6LU7) using Glide docking; however, the docking scores and binding affinity were poorly correlated. Thus, the authors concluded that docking was not a viable virtual screening approach for Mpro of SARS-CoV-2.

Yoshino et al. reported identification of key pharmacophoric features that contribute to interaction between SARS-CoV-2 Mpro and its inhibitors.138  Co-crystals from Mpro of SARS-CoV (PDB ID: 2A5I, 2OP9) and SARS-CoV-2 Mpro (PDB ID: 6LU7) were used to extract pharmacophores in Phase program. Indinavir was aligned to the pharmacophore features and subjected to hydrogen bond optimization, energy minimization and MD simulations for 1 µs under the isothermal-isobaric NPT ensemble. Indinavir satisfies all criteria and was hence suggested by the authors as a drug against SARS-CoV-2.

Various advanced methods such as network-based methods, machine learning and artificial intelligence have been applied for repurposing of drugs against COVID-19. This section summarizes of efforts reported in the literature using these methods.

Machine learning methods have been applied to quickly identify drugs against SARS-CoV-2. Details of these machine learning efforts are summarized in Table 1.20; these efforts used either the molecular descriptors or docking interactions for machine learning.

Table 1.20

Summary of machine learning studiesa

PDB/HMDatabase usedMethod appliedDescriptorsHitsReference
 UNII Database, DrugBank, Therapeutic Targets Database Support Vector Machine (SVM) ∼5300 AlvaDesc descriptors Phenazopyridine, abemaciclib, promazine, tykerb, pirenzepine, ebastine, alectinib lestaurtinib, vorinostat, cefmenoxime, lifitegrast, vemurafenib, selimexor, amorolfine, dacomitinib, enalaprilat 139  
 DrugBank Deep Neural Network (DNN) 613 molecular descriptors Boceprevir, chloroquine, homoharringtonine, tilorone, salinomycin 140  
6LU7 FDA-approved drugs Molecule Transformer-Drug Target Interaction (MT-DTI) Drug–target interactions Atazanavir, remdesivir, efavirenz, ritonavir, dolutegravir 141  
3TNT Chimdiv; Targetmols, database of tripeptides Dense Fully Convolutional Neural Network (DFCNN) Drug–target interactions Meglumine, adenosine, vidarabine, d-sorbitol, sodium gluconate, d-mannitol, ganciclovir, chlorobutanol 142  
PDB/HMDatabase usedMethod appliedDescriptorsHitsReference
 UNII Database, DrugBank, Therapeutic Targets Database Support Vector Machine (SVM) ∼5300 AlvaDesc descriptors Phenazopyridine, abemaciclib, promazine, tykerb, pirenzepine, ebastine, alectinib lestaurtinib, vorinostat, cefmenoxime, lifitegrast, vemurafenib, selimexor, amorolfine, dacomitinib, enalaprilat 139  
 DrugBank Deep Neural Network (DNN) 613 molecular descriptors Boceprevir, chloroquine, homoharringtonine, tilorone, salinomycin 140  
6LU7 FDA-approved drugs Molecule Transformer-Drug Target Interaction (MT-DTI) Drug–target interactions Atazanavir, remdesivir, efavirenz, ritonavir, dolutegravir 141  
3TNT Chimdiv; Targetmols, database of tripeptides Dense Fully Convolutional Neural Network (DFCNN) Drug–target interactions Meglumine, adenosine, vidarabine, d-sorbitol, sodium gluconate, d-mannitol, ganciclovir, chlorobutanol 142  
a

Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay. Drugs in bold italics have both the assays reported.

Kowalewski and Ray reported application of machine learning to identify several drug candidates against COVID-19.139  Assay data of 24 human targets related to SARS-CoV-2 were used to train machine learning models for predicting drugs. Out of the predicted drugs phenazopyridine (EC50 28 µM), abemaciclib (EC50 43.7 µM) and ebastine (EC50 6.92 µM) were reported to inhibit the cellular SARS-CoV-2 infection. Ke et al. reported application of an artificial intelligence (AI)-based platform to identify potential drugs for COVID-19.140  The authors used a unique approach of re-learning the models after testing promising hits against feline infectious peritonitis. Drugs reported to inhibit SARS-CoV-2 in cellular assay are chloroquine (EC50 1.13 µM), homoharringtonine (EC50 2.55 µM), tilorone (EC50 4.09 µM) and salinomycin (EC50 0.24 µM), while chloroquine (EC50 7.16 µM) and boceprevir (EC50 0.95 µM) are reported to inhibit 3CLpro. Both articles reported models that were able to predict compounds acting against SARS-CoV-2, thus demonstrating the promise of machine learning methods.

Beck et al. reported use of a pre-trained deep learning model to repurpose drugs inhibiting 3CLpro for SARS-CoV-2.141  The hits predicted from this approach are atazanavir (EC50 9.36 µM), remdesivir (EC50 0.62 µM), ritonavir (EC50 1 µM) and dolutegravir (EC50 22.04 µM), which have been found active in cellular assay of SARS-CoV-2. However, remdesivir is known to act via RdRp inhibition and its ability to inhibit 3CLpro can be confirmed only after experimental evidence. Zhang et al. reported repurposing using a deep learning based method in 3CLpro with the help of drug–receptor interactions.142  Valganciclovir, a prodrug of ganciclovir has been tested in 3CLpro of SARS-CoV-2 with IC50 of 16.66 µM.

Pharmacology-based network analysis methods also have been utilized in the quest of repurposing drugs against SARS-CoV-2. The network analysis was performed either by utilizing protein–protein interactions or gene expression profiling.

Gysi et al. identified promising drugs using network proximity, diffusion and AI-based metrics.143  A list of 81 repurposed drugs was finalized after prioritization and screening. Such an extensive list of hits has limited relevance to the pandemic, as computational methods are expected to pinpoint towards effective drugs for specific targets for experimental testing. Zhou et al. reported use of systems pharmacology based network medicine for repurposing drugs for COVID-19.144  The network-based proximity analyses suggested repurposable candidates using a gene set enrichment analysis (GSEA) score. Out of 16 predicted opportunities, viz. mesalazine, toremifene (EC50 11.3 µM), eplerenone, paroxetine, sirolimus, dactinomycin, irbesertan, mercaptopurine, melatonin, quniacrine, carvedilol, colchicine, camphor, equilin, oxymetholone and emodin, only one drug was tested for SARS-CoV-2 inhibition. Furthermore, Cava et al. presented repurposing drugs for COVID-19, by building correlation between ACE-2 and genes in TCGA-LUAD.145  A protein–protein interaction network was constructed considering interactions among ACE-2-correlated genes. None of the predicted drugs nimesulide, fluticasone, thiabendazole, photofrin, didanosine and flutamide was tested against SARS-CoV-2 infection.

Glinsky et al.146  reported identification of repressors and activators of ACE-2 and FURIN using GSEA scores and expression profiling experiments. This suggested that quercetin and vitamin D, respectively, alter significant numbers of human and SARS-CoV-2 proteins which are relevant for COVID-19. Two interventional randomized clinical trials evaluating effects of vitamin D on prevention and treatment of COVID-19 are listed on ClinicalTrials.gov. Furthermore, vitamin D is being routinely prescribed by clinicians to reduce ill effects of COVID-19.

Viruses are unique parasites which contain genetic material encapsulated in a protein shell and can only replicate inside a living host. Since they lack metabolic machinery of their own, they are totally dependent on the host cell for completing their life cycle. Post-infection the virus utilizes the host machinery for attachment and penetration into the host cells, replication and assembly, and finally is released to infect other cells. Given the criticality of host targets in the viral life cycle, host factors could be important targets for COVID-19. Exploring host targets or pathways that are critical in COVID-19 has some benefits,147  as follows.

  1. Host targets may provide a broader antiviral spectrum, e.g. drug acting on a particular host target can provide protection against all viruses dependent on that target. Converesly, viruses have very high mutation rates, which pose challenges in terms of resistance to drugs in the long term.

  2. Host targets involved in progression of the disease can be targeted so that worsening of disease can be controlled, e.g. targets involved in ARDS or targets which can activate host immune response.

  3. Host targets can have known drugs acting on these targets, which can make repurposing easier and faster.

The entry of SARS-CoV-2 into the host cells via the spike protein is discussed in Section 1.2.1. Post-attachment, the SARS-CoV-2 is internalized and releases the genetic material (RNA) into the host cell cytoplasm either by fusion at the cell plasma membrane or engulfing of the ACE-2 and SARS-CoV-2 bound complex. The virus then smartly utilizes the host machinery for transcription, translation, post-translational modifications and finally release to infect adjoining cells.

SARS-CoV-2 infection can activate the host immune responses, which in severe cases could result in exhausted cytotoxic lymphocytes, lymphocytopenia and a severe cytokine storm. Higher levels of pro-inflammatory cytokines and chemokines, viz. interleukin (IL)-17, IL-2, IL-7, IL-6, granulocyte colony-stimulating factor (G-CSF), IP-10, monocyte chemoattractant protein (MCP)-1, macrophage inflammatory protein (MIP)1A and tumour necrosis factor (TNF)-α are reported in a typical cytokine storm. The cytokine storm also results in neutrophils and macrophage infiltration, diffused alveolar damage and a diffused thickening of the alveolar wall. This may result into viral sepsis, pneumonitis, inflammation-induced lung injury, ARDS, respiratory failure, shock, multiple organ failure and potential death.148–151 

Understanding the modus operandi of SARS-CoV-2 to utilize human machinery to invade the immune system and replicate and the response of human body to the infection is critical in drug repurposing for COVID-19.

Crystal structures for most of the human targets involved in attachment of SARS-CoV-2 and host response to infection are already available on www.rcsb.org. Currently, there are 159 structures reported in PDB and Table 1.21 provides structural information of human targets involved in COVID-19.

Table 1.21

Human protein structures associated with COVID-19 available in PDB

Macromolecule nameNo of structures in PDBResolution rangeNo of ligands
ACE-2 14 2.2 to 3.31 Å 13 
Furin 20 1.8 to 2.7 Å 16 
GM-CSF 10 2.0 to 3.3 Å 
GRP78 21 1.6 to 2.9 Å 18 
IL-17 13 1.7 to 3.3 Å 
IL-2 22 1.9 to 3.8 Å 12 
IL-6 1.9 to 3.6 Å 
IL-7 2.7 to 2.9 Å 
IP-10 1.92 to 3 Å 
MCP-1 1.85 to 2.8 Å 
MIP1 1.76 to 3.1 Å 
TNF-α 27 1.4 to 3.1 Å 14 
Macromolecule nameNo of structures in PDBResolution rangeNo of ligands
ACE-2 14 2.2 to 3.31 Å 13 
Furin 20 1.8 to 2.7 Å 16 
GM-CSF 10 2.0 to 3.3 Å 
GRP78 21 1.6 to 2.9 Å 18 
IL-17 13 1.7 to 3.3 Å 
IL-2 22 1.9 to 3.8 Å 12 
IL-6 1.9 to 3.6 Å 
IL-7 2.7 to 2.9 Å 
IP-10 1.92 to 3 Å 
MCP-1 1.85 to 2.8 Å 
MIP1 1.76 to 3.1 Å 
TNF-α 27 1.4 to 3.1 Å 14 

The key human targets that can be utilized for computational repurposing of drugs for COVID-19 are listed in Table 1.22.

Table 1.22

Details of human protein structures associated with COVID-19 available in PDB

PDBResolution (Å)Target nameLigand IDPDBResolution (Å)Target nameLigand ID
1R42 2.20 ACE-2  5LQB 1.95 IL-2 NARA1a 
1R4L 3.00 ACE-2 XX5 1M47 1.99 IL-2  
2AJF 2.90 ACE-2 SPIKEa 1M49 2.00 IL-2 CMM 
3D0G 2.80 ACE-2 SPIKEa 1M4B 2.15 IL-2 NMP 
3D0H 3.10 ACE-2 SPIKEa 1M4A 2.18 IL-2 MPE 
3D0I 2.90 ACE-2 SPIKEa 1NBP 2.20 IL-2 MHC 
3KBH 3.31 ACE-2 SPIKEa 2B5I 2.30 IL-2  
3SCI 2.90 ACE-2 SPIKEa 5M5E 2.30 IL-2  
3SCJ 3.00 ACE-2 SPIKEa 1M4C 2.40 IL-2  
3SCK 3.00 ACE-2 SPIKEa 3INK 2.50 IL-2  
3SCL 3.00 ACE-2 SPIKEa 1PW6 2.60 IL-2 FRB 
6LZG 2.50 ACE-2 SPIKEa 1QVN 2.70 IL-2 FRI 
6M0J 2.45 ACE-2 SPIKEa 5UTZ 2.75 IL-2 Fab 5111a 
6VW1 2.68 ACE-2 SPIKEa 1PY2 2.80 IL-2 FRH 
5E84 2.99 GRP78 ATP 1Z92 2.80 IL-2  
5E85 2.57 GRP78  2ERJ 3.00 IL-2  
5E86 2.68 GRP78  3QB1 3.10 IL-2  
3IUC 2.40 GRP78 ADP 6VWU 3.40 IL-2  
3LDL 2.30 GRP78 ATP 3QAZ 3.80 IL-2  
3LDN 2.20 GRP78  3DI2 2.70 IL-7  
3LDO 1.95 GRP78 ANP 3DI3 2.90 IL-7  
3LDP 2.20 GRP78 3P1 1CSG 2.7 GM-CSF  
5EVZ 1.85 GRP78 ADP 2GMF 2.4 GM-CSF  
6ASY 1.85 GRP78 ATP 4NKQ 3.3 GM-CSF  
6CZ1 1.68 GRP78 3FD 4RS1 2.7 GM-CSF  
6DFM 2.14 GRP78 3BH 5C7X 3.0 GM-CSF G1 Faba 
6DFO 2.54 GRP78 GBA 5D70 2.1 GM-CSF G1 Faba 
6DO2 1.70 GRP78 H5V 5D71 2.3 GM-CSF G1 Faba 
6DWS 1.90 GRP78 HFY 5D72 2.6 GM-CSF G1 Faba 
5F0X 1.60 GRP78 DTP 6BFQ 2.6 GM-CSF Faba 
5F1X 1.90 GRP78 ATP 6BFS 2.0 GM-CSF Faba 
5F2R 2.15 GRP78 ACP 1O7Y 3.00 IP-10  
5EX5 1.90 GRP78 7DD 1O7Z 1.92 IP-10  
5EXW 1.90 GRP78 7DT 1O80 2.00 IP-10  
5EY4 1.86 GRP78 DTP 1DOK 1.85 MCP-1  
1ALU 1.90 IL-6  3IFD 1.90 MCP-1  
1P9M 3.56 IL-6  4R8I 2.05 MCP-1  
4CNI 2.20 IL-6 Olokizumaba 1DOL 2.40 MCP-1  
4J4L 2.30 IL-6  2NZ1 2.50 MCP-1  
4NI7 2.40 IL-6  2BDN 2.53 MCP-1 11K2a 
4NI9 2.55 IL-6  4ZK9 2.60 MCP-1  
4O9H 2.42 IL-6 Camelida 1ML0 2.80 MCP-1 M3a 
4ZS7 2.93 IL-6 Llamaa 4DN4 2.80 MCP-1 CNTO 888a 
5FUC 2.70 IL-6  3FPU 1.76 MIP1 Evasin-1a 
4OMC 2.30 Furin  2X6G 2.18 MIP1  
4OMD 2.70 Furin Phac-RVR-Amba a 5COR 2.55 MIP1  
4RYD 2.15 Furin para-guanidinomethyl-Phac-R-Tle-R-Ambaa 4RA8 2.60 MIP1  
4Z2A 1.89 Furin  2X69 2.65 MIP1  
5JMO 2.00 Furin Nb14a 3KBX 2.65 MIP1  
5JXG 1.80 Furin  4ZKB 2.90 MIP1  
5JXH 2.00 Furin meta-guanidinomethyl-Phac-RVR-Ambaa 3H44 3.00 MIP1  
5JXI 2.00 Furin  5D65 3.10 MIP1  
5JXJ 2.00 Furin meta-guanidinomethyl-Phac-RVR-Ambaa 5UUI 1.40 TNF-α MTN 
5MIM 1.90 Furin 1N 4Y6O 1.60 TNF-α  
6EQV 1.90 Furin Phac-Cit-Val-Arg-Ambaa 2E7A 1.80 TNF-α  
6EQW 1.99 Furin 4-aminomethyl-phenylacetyl-Arg-Val-Arg-Ambaa 4TSV 1.80 TNF-α  
6EQX 1.99 Furin Arg-Arg-Arg-Val-Arg-Ambaa 5M2J 1.90 TNF-α Llama VHH2a 
6HLB 2.00 Furin c[succinyl-Phe-2-Nal-(Arg)4-Lys]-Arg-4-Ambaa 2AZ5 2.10 TNF-α 307 
6HLD 2.10 Furin c[succinyl-Phe-2-Nal-(Arg)3-Lys]-Lys-4-Ambaa 3L9J 2.10 TNF-α  
6HLE 1.99 Furin H-Lys-Arg-Arg-Tle-Lys-4-Ambaa 5M2I 2.15 TNF-α Llama VHH2a 
6HZA 1.90 Furin c[glutaryl-Arg-Arg-Lys]-Arg-4-Ambaa 1A8M 2.30 TNF-α  
6HZB 1.90 Furin c[glutaryl-Arg-Arg-Lys]-Lys-4-Ambaa 5M2M 2.30 TNF-α Llama VHH2a 
6HZC 1.90 Furin c[glutaryl-BVK-Lys-Arg-Arg-Tle-Lys]-4-Ambaa 2ZJC 2.50 TNF-α  
6HZD 1.90 Furin c[glutaryl-Arg-Arg-Arg-Lys]-Arg-4-Ambaa 5TSW 2.50 TNF-α  
2VXS 2.63 IL-17 CAT-2200a 6OOY 2.50 TNF-α A7M 
4HR9 2.48 IL-17  6OP0 2.55 TNF-α A7A 
4HSA 3.15 IL-17  1TNF 2.60 TNF-α  
4QHU 2.20 IL-17 Fab6785a 4G3Y 2.60 TNF-α Infliximaba 
5HHV 2.20 IL-17 IL-17RAa 6RMJ 2.65 TNF-α  
5HHX 3.00 IL-17 IL-17RAa 5YOY 2.73 TNF-α Golimumaba 
5HI3 2.15 IL-17 63O 3IT8 2.80 TNF-α  
5HI4 1.80 IL-17 63P 6OOZ 2.80 TNF-α A6Y 
5HI5 1.80 IL-17 63Q 2ZPX 2.83 TNF-α  
5N7W 1.96 IL-17  4TWT 2.85 TNF-α M21 
5N92 2.30 IL-17  5WUX 2.90 TNF-α Certolizumaba 
5NAN 3.30 IL-17 IL-17RAa 3ALQ 3.00 TNF-α  
5VB9 1.70 IL-17 Peptide inhibitora 5MU8 3.00 TNF-α JNI 
4NEJ 1.92 IL-2 2K1 2TUN 3.10 TNF-α  
4NEM 1.93 IL-2 2JY 3WD5 3.10 TNF-α Adalimumaba 
1M48 1.95 IL-2 FRG     
PDBResolution (Å)Target nameLigand IDPDBResolution (Å)Target nameLigand ID
1R42 2.20 ACE-2  5LQB 1.95 IL-2 NARA1a 
1R4L 3.00 ACE-2 XX5 1M47 1.99 IL-2  
2AJF 2.90 ACE-2 SPIKEa 1M49 2.00 IL-2 CMM 
3D0G 2.80 ACE-2 SPIKEa 1M4B 2.15 IL-2 NMP 
3D0H 3.10 ACE-2 SPIKEa 1M4A 2.18 IL-2 MPE 
3D0I 2.90 ACE-2 SPIKEa 1NBP 2.20 IL-2 MHC 
3KBH 3.31 ACE-2 SPIKEa 2B5I 2.30 IL-2  
3SCI 2.90 ACE-2 SPIKEa 5M5E 2.30 IL-2  
3SCJ 3.00 ACE-2 SPIKEa 1M4C 2.40 IL-2  
3SCK 3.00 ACE-2 SPIKEa 3INK 2.50 IL-2  
3SCL 3.00 ACE-2 SPIKEa 1PW6 2.60 IL-2 FRB 
6LZG 2.50 ACE-2 SPIKEa 1QVN 2.70 IL-2 FRI 
6M0J 2.45 ACE-2 SPIKEa 5UTZ 2.75 IL-2 Fab 5111a 
6VW1 2.68 ACE-2 SPIKEa 1PY2 2.80 IL-2 FRH 
5E84 2.99 GRP78 ATP 1Z92 2.80 IL-2  
5E85 2.57 GRP78  2ERJ 3.00 IL-2  
5E86 2.68 GRP78  3QB1 3.10 IL-2  
3IUC 2.40 GRP78 ADP 6VWU 3.40 IL-2  
3LDL 2.30 GRP78 ATP 3QAZ 3.80 IL-2  
3LDN 2.20 GRP78  3DI2 2.70 IL-7  
3LDO 1.95 GRP78 ANP 3DI3 2.90 IL-7  
3LDP 2.20 GRP78 3P1 1CSG 2.7 GM-CSF  
5EVZ 1.85 GRP78 ADP 2GMF 2.4 GM-CSF  
6ASY 1.85 GRP78 ATP 4NKQ 3.3 GM-CSF  
6CZ1 1.68 GRP78 3FD 4RS1 2.7 GM-CSF  
6DFM 2.14 GRP78 3BH 5C7X 3.0 GM-CSF G1 Faba 
6DFO 2.54 GRP78 GBA 5D70 2.1 GM-CSF G1 Faba 
6DO2 1.70 GRP78 H5V 5D71 2.3 GM-CSF G1 Faba 
6DWS 1.90 GRP78 HFY 5D72 2.6 GM-CSF G1 Faba 
5F0X 1.60 GRP78 DTP 6BFQ 2.6 GM-CSF Faba 
5F1X 1.90 GRP78 ATP 6BFS 2.0 GM-CSF Faba 
5F2R 2.15 GRP78 ACP 1O7Y 3.00 IP-10  
5EX5 1.90 GRP78 7DD 1O7Z 1.92 IP-10  
5EXW 1.90 GRP78 7DT 1O80 2.00 IP-10  
5EY4 1.86 GRP78 DTP 1DOK 1.85 MCP-1  
1ALU 1.90 IL-6  3IFD 1.90 MCP-1  
1P9M 3.56 IL-6  4R8I 2.05 MCP-1  
4CNI 2.20 IL-6 Olokizumaba 1DOL 2.40 MCP-1  
4J4L 2.30 IL-6  2NZ1 2.50 MCP-1  
4NI7 2.40 IL-6  2BDN 2.53 MCP-1 11K2a 
4NI9 2.55 IL-6  4ZK9 2.60 MCP-1  
4O9H 2.42 IL-6 Camelida 1ML0 2.80 MCP-1 M3a 
4ZS7 2.93 IL-6 Llamaa 4DN4 2.80 MCP-1 CNTO 888a 
5FUC 2.70 IL-6  3FPU 1.76 MIP1 Evasin-1a 
4OMC 2.30 Furin  2X6G 2.18 MIP1  
4OMD 2.70 Furin Phac-RVR-Amba a 5COR 2.55 MIP1  
4RYD 2.15 Furin para-guanidinomethyl-Phac-R-Tle-R-Ambaa 4RA8 2.60 MIP1  
4Z2A 1.89 Furin  2X69 2.65 MIP1  
5JMO 2.00 Furin Nb14a 3KBX 2.65 MIP1  
5JXG 1.80 Furin  4ZKB 2.90 MIP1  
5JXH 2.00 Furin meta-guanidinomethyl-Phac-RVR-Ambaa 3H44 3.00 MIP1  
5JXI 2.00 Furin  5D65 3.10 MIP1  
5JXJ 2.00 Furin meta-guanidinomethyl-Phac-RVR-Ambaa 5UUI 1.40 TNF-α MTN 
5MIM 1.90 Furin 1N 4Y6O 1.60 TNF-α  
6EQV 1.90 Furin Phac-Cit-Val-Arg-Ambaa 2E7A 1.80 TNF-α  
6EQW 1.99 Furin 4-aminomethyl-phenylacetyl-Arg-Val-Arg-Ambaa 4TSV 1.80 TNF-α  
6EQX 1.99 Furin Arg-Arg-Arg-Val-Arg-Ambaa 5M2J 1.90 TNF-α Llama VHH2a 
6HLB 2.00 Furin c[succinyl-Phe-2-Nal-(Arg)4-Lys]-Arg-4-Ambaa 2AZ5 2.10 TNF-α 307 
6HLD 2.10 Furin c[succinyl-Phe-2-Nal-(Arg)3-Lys]-Lys-4-Ambaa 3L9J 2.10 TNF-α  
6HLE 1.99 Furin H-Lys-Arg-Arg-Tle-Lys-4-Ambaa 5M2I 2.15 TNF-α Llama VHH2a 
6HZA 1.90 Furin c[glutaryl-Arg-Arg-Lys]-Arg-4-Ambaa 1A8M 2.30 TNF-α  
6HZB 1.90 Furin c[glutaryl-Arg-Arg-Lys]-Lys-4-Ambaa 5M2M 2.30 TNF-α Llama VHH2a 
6HZC 1.90 Furin c[glutaryl-BVK-Lys-Arg-Arg-Tle-Lys]-4-Ambaa 2ZJC 2.50 TNF-α  
6HZD 1.90 Furin c[glutaryl-Arg-Arg-Arg-Lys]-Arg-4-Ambaa 5TSW 2.50 TNF-α  
2VXS 2.63 IL-17 CAT-2200a 6OOY 2.50 TNF-α A7M 
4HR9 2.48 IL-17  6OP0 2.55 TNF-α A7A 
4HSA 3.15 IL-17  1TNF 2.60 TNF-α  
4QHU 2.20 IL-17 Fab6785a 4G3Y 2.60 TNF-α Infliximaba 
5HHV 2.20 IL-17 IL-17RAa 6RMJ 2.65 TNF-α  
5HHX 3.00 IL-17 IL-17RAa 5YOY 2.73 TNF-α Golimumaba 
5HI3 2.15 IL-17 63O 3IT8 2.80 TNF-α  
5HI4 1.80 IL-17 63P 6OOZ 2.80 TNF-α A6Y 
5HI5 1.80 IL-17 63Q 2ZPX 2.83 TNF-α  
5N7W 1.96 IL-17  4TWT 2.85 TNF-α M21 
5N92 2.30 IL-17  5WUX 2.90 TNF-α Certolizumaba 
5NAN 3.30 IL-17 IL-17RAa 3ALQ 3.00 TNF-α  
5VB9 1.70 IL-17 Peptide inhibitora 5MU8 3.00 TNF-α JNI 
4NEJ 1.92 IL-2 2K1 2TUN 3.10 TNF-α  
4NEM 1.93 IL-2 2JY 3WD5 3.10 TNF-α Adalimumaba 
1M48 1.95 IL-2 FRG     
a

Co-crystal ligands are proteins.

Computational approaches for drug repurposing are more useful in terms of shortlisting the candidates for experimental verification or proposing mechanism of action of selected drugs in disease. Some such notable attempts reported in the literature are summarized in this section.

ACE-2 is a key human target for repurposing of drugs, since it is involved in entry of SARS-CoV-2 in the human cells.152,153  Various in silico attempts reported for repurposing are summarized in Table 1.23.

Table 1.23

Summary of efforts targeting ACE-2a

PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
1R4L Library of 7173 ligands Auto Dock Vina    Lividomycin, burixafor, fluprofylline, quisinostat, spirofylline, edotecarin, pemetrexed, diniprofylline 154  
1r42, 6m0j ACE inhibitiors MOE MOE 600 ps MM-GBSA Ramipril, delapril, lisinopril 155  
2AJF, 6VSB LOPAC library AutoDock Vina GROMACS 50 ns  GR 127935, hydrochloride hydrate, GNF-5, RS504393, TNP, eptifibatide acetate 156  
6MJ0 78 small molecules AutoDock    Remdesivir, oseltamivir, zanamivir 157  
PDB IDDatabase usedDocking toolMD programMD timeFree energyHitsReference
1R4L Library of 7173 ligands Auto Dock Vina    Lividomycin, burixafor, fluprofylline, quisinostat, spirofylline, edotecarin, pemetrexed, diniprofylline 154  
1r42, 6m0j ACE inhibitiors MOE MOE 600 ps MM-GBSA Ramipril, delapril, lisinopril 155  
2AJF, 6VSB LOPAC library AutoDock Vina GROMACS 50 ns  GR 127935, hydrochloride hydrate, GNF-5, RS504393, TNP, eptifibatide acetate 156  
6MJ0 78 small molecules AutoDock    Remdesivir, oseltamivir, zanamivir 157  
a

Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.

Terali et al. reported an in silico study to identify viral-entry inhibitors.154  The authors propose that the hits can stabilize the closed conformation of ACE-2, thereby modulating the interaction of ACE-2 with spike protein. This is yet to be experimentally validated.

Khelfaoui et al. reported screening of 18 drugs by molecular docking as inhibitors of ACE-2 and the spike–ACE-2 interface.155  Global reactivity descriptors were used for filtering followed by the MD study. Drugs proposed in this study are already approved as ACE inhibitors and none of them is reported to inhibit ACE-2. Choudhary et al. explored the spike–ACE-2 receptor complex for repurposing drugs from the LOPAC library.156  None of the proposed drugs in either articles have been experimentally validated for their actions.

Zhou et al. reported utilization of KATZ (a network-based association prediction method)-based virus–drug associations.157  The top 10 hits from the network associations were docked in spike and ACE-2 receptors to finalize the hits, which included remdesivir (RdRp inhibitor). There is no experimental evidence to conclude that remdesivir inhibits spike–ACE-2 interactions.

A key molecular target transmembrane protease, serine 2 (TMPRSS2) which is evidenced to assist the entry of SARS-CoV-2 in the human cells has been investigated for drug repurposing. Details of these efforts of drug repurposing are listed in Table 1.24.

Table 1.24

Summary of efforts targeting ACE-2

TemplateHM toolSimilarityDatabase UsedDocking ToolMD ProgramMD TimeFree EnergyHitsReference
6O1G Swiss Model 44% Drugs-lib, AIEfd-Db AutoDock Vina     158  
2ANY, 5TJX, 6O1G, 4NA8, 5CE1, 1O5E, 3W94 Prime  19 serine protease inhibitors Glide    Argatroban, otamixaban, letaxaban, darexaban, edoxaban, nafamostat 159  
5CE1 Swiss Model 35.2% 6654 small molecules Maestro Desmond 10 ns MM-GBSA Benzquercin 160  
1Z8G Swiss Model 35.2% SelleckChem MOE AMBER 18 100 ns MM-GBSA Neohesperidin, myricitrin, quercitrin, naringin, icariin, ambroxol 161  
TemplateHM toolSimilarityDatabase UsedDocking ToolMD ProgramMD TimeFree EnergyHitsReference
6O1G Swiss Model 44% Drugs-lib, AIEfd-Db AutoDock Vina     158  
2ANY, 5TJX, 6O1G, 4NA8, 5CE1, 1O5E, 3W94 Prime  19 serine protease inhibitors Glide    Argatroban, otamixaban, letaxaban, darexaban, edoxaban, nafamostat 159  
5CE1 Swiss Model 35.2% 6654 small molecules Maestro Desmond 10 ns MM-GBSA Benzquercin 160  
1Z8G Swiss Model 35.2% SelleckChem MOE AMBER 18 100 ns MM-GBSA Neohesperidin, myricitrin, quercitrin, naringin, icariin, ambroxol 161  

Two studies reported screening by only docking methods.158,159  Singh et al. reported repositioning of approved, investigational and experimental drugs inhibiting TMPRSS2.158  Based on the analysis, the authors proposed 156 hits binding to the catalytic site and 100 hits binding to the exosite. In silico drug repurposing is expected to provide shortlisted drug candidates as help to experimentalists. Providing such a huge list of drugs for any target as proposed by the authors does not help main purpose of in silico screening. Rensi et al. explored repurposing of drug candidates binding to TMPRSS2.159  Nafamostat, which emerges as a hit, is a known inhibitor of TMPRSS2, while other compounds have not been tested specifically against the target.

Furthermore, two studies involved screening by docking and MD methods.160,161  Durdaği160  proposed benzquercin as hit, which has not been tested against TMPRSS2. Chikhale et al. reported structure-based screening to find out potential drugs as TMPRSS2 inhibitors.161  None of the proposed hit drugs has been tested against TMPRSS2.

Palmeira et al. reported virtual screening of 10 761 drugs (DrugBank) to identify drugs acting against NBD of GRP78 (PDB ID: 5E84) using MOE software.162  The most promising candidates identified are imatinib, selonsertib, zilucoplan, obinepitide and corticorelin ovine triflutate. These compounds have not been tested against GRP78. The involvement of this target in attachment of spike protein is a theoretical possibility as reported by the authors, but there is no evidence to date that the spike protein attaches to GRP78.

Villoutreix et al. reported virtual screening of ∼8000 drugs from different databases using Surflex-Dock (https://www.biopharmics.com/) to identify drugs that could inhibit furin.163  Docking studies in the furin (PDB ID: 5JXH, 5MIM) resulted in identifying sulconazole as the most promising drug. Sulconazole inhibited the furin-mediated cleavage of the substrate MT1-MMP, suggesting that sulconazole inhibits furin and can be repurposed for COVID-19. This study clearly demonstrates success of in silico methods in identifying potential hits.

Leung et al. reported structure-based virtual screening of 3000 US FDA-approved drugs against TNF-α (PDB ID: 2AZ5) using the ICM-Pro program (http://www.molsoft.com/).164  Darifenacin and ezetimibe were predicted to directly inhibit TNF-α. Ezetimibe is also known to reduce TNF-α expression in macrophages.165  Although this study was not specifically reported for repurposing drugs for COVID-19, it has been reported here as TNF-α inhibition can be useful for alleviating ARDS in COVID-19. A recent cohort study showed that ezetimibe reduced hospitalization risk and a promising protective effect against COVID-19.166 

In summary, a few human targets have been studied computationally for repurposing drugs against SARS-CoV-2. Most of the studies reported use of AutoDock, ICM, MOE, Discovery Studio and Glide for docking. Some of the authors have performed MD simulations to validate or reaffirm the ability of ligands to bind to the potential targets. Most of the drugs which were repurposed computationally have not yet been tested for the target for which they have been repurposed or in COVID-19 patients.

This chapter reviews approximately 130 research papers published in peer-reviewed journals which use in silico methods for drug repurposing. The hits suggested as promising in the respective papers have been assessed and comments are provided where relevant. A target-wise collection of hits identified using different in silico methods has been collated and is presented in Table 1.25. This table also provides number of papers which report suggested drug as a hit (termed as frequency in Table 1.25). The validation of hits in terms of reported experimental assays, either specific target inhibition of SARS-CoV-2 or cellular inhibition of SARS-CoV-2, has also been provided in the table.

Table 1.25

Target-wise summary of repurposed drugs reported by in silico methodsa

Target nameDrugs (frequency)bReferences that report hits
mPRO Saquinavir (14), ritonavir (12), lopinavir (11), nelfinavir (9), indinavir (8), remdesivir (7), simeprevir (7), cobicistat (5), darunavir (5), paritaprevir (5), raltegravir (5), glecaprevir (4), ribavirin (4), amprenavir (3), apixaban (3), disulfiram (3), ledipasvir (3), tipranavir (3), acarbose (2), afatinib (2), atazanavir (2), azelastine (2), azithromycin (2), betrixaban (2), birinapant (2), bromocriptine (2), cangrelor (2), carfilzomib (2), chloroquine (2), cinanserin (2), colistin (2), conivaptan (2), daclatasvir (2), demeclocycline (2), dihydroergotamine (2), dolutegravir (2), doxycycline (2), ebselen (2), edoxaban (2), ergoloid (2), ergotamine (2), grazoprevir (2), hesperidin (2), hydroxychloroquine (2), mefloquine (2), montelukast (2), octreotide (2), oxytetracycline (2), perampanel (2), rivaroxaban (2), shikonin (2), tadalafil (2), telaprevir (2), telmisartan (2), teniposide (2), tetracycline (2), tetrandrine (2), tideglusib (2), tolvaptan (2), valrubicin (2), amodiaquine (1), boceprevir (1), carprofen (1), celecoxib (1), cenicriviroc (1), emetine (1), fluvastatin (1), itraconazole (1), ivermectin (1), proglumetacin (1), sufugolix (1) 26, 27, 29, 31–79, 107–110, 113–123, 125–132, 136–138  
RdRp Remdesivir (7), sofosbuvir (4), galidesivir (3), ribavirin (3), saquinavir (3), cepharanthine (2), colistin (2), dihydroergotamine (2), examorelin (2), favipiravir (2), lypressin (2), novobiocin (2), ornipressin (2), polymyxin b1 (2), simeprevir (2), tenofovir (2), hydroxychloroquine (1) 80, 82–88, 115, 116, 120–122, 126, 127, 129 and 134  
PLproc Ribavirin (3), lopinavir (2), nelfinavir (2), paritaprevir (2), ritonavir (2), cefamandole (1), chloramphenicol (1), chloroquine (1), chlorphenesin (1), darolutamide (1), famotidine (1), glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), ketoamide (1), ledipasvir (1), levodropropizine (1), luteolin (1), phenformin (1), quercetin (1), remdesivir (1), simeprevir (1), thymidine (1), tigecycline (1), tipranavir (1), valganciclovir (1), ziprasidone (1) 90, 91, 113–115, 118, 127, 129 and 131  
N-protein Chloroquine (1), grazoprevir (1), hydroxychloroquine (1), radotinib (1), ribavirin (1), simeprevir (1), valganciclovir (1), zidovudine (1) 92, 93, 112 and 116  
SPIKE Chloroquine (2), glycyrrhizic acid (2), nilotinib (2), azithromycin (1), digitoxin (1), doxycycline (1), lopinavir (1), nelfinavir (1), posaconazole (1), remdesivir (1), ritonavir (1), saquinavir (1) 94–98, 107–110, 115–118, 127, 131, 132, 134 and 135  
NSP1 Cefpiramide (1), cidofovir (1), edoxudine (1), esculin (1), lactose (1), lymecycline (1), phthalocyanine (1), piperacillin (1), remdesivir (1), streptomycin (1), tetracycline (1), tirilazad (1) 99, 100 and 115  
NSP13 Atazanavir (1), bictegravir (1), canrenoic acid (1), cefsulodin (1), dabigatran (1), grazoprevir (1), itraconazole (1), lymecycline (1), meclonazepam (1), oxiphenisatin (1), paritaprevir (1), rolitetracycline (1), simeprevir (1), vapreotide (1) 101, 115, 116, 120 and 133  
NSP15 Arbidol (1), bictegravir (1), dihydroergotamine (1), ergotamine (1), glisoxepide (1), idarubicin (1), nilotinib (1), remdesivir (1), tasosartan (1) 102, 103, 115 and 116  
NSP16 Dolutegravir (2), saquinavir (2), bictegravir (1), cangrelor (1), digitoxin (1), dihydroergotamine (1), eriodictyol (1), favipiravir (1), fludarabine (1), irinotecan (1), maraviroc (1), montelukast (1), prednisolone (1), raltegravir (1), raltgravir (1), ritonavir (1), sinefungin (1), teniposide (1) 104–106, 116, 117 and 130  
NSP10/NSP16 2-deoxyglucose (1), daphnetin (1), methylgallate (1), oxytetracycline (1), telbivudine (1) 125  
E-protein Beclabuvir (1), doxycycline (1), mycophenolic acid (1), rutin (1) 111, 112 and 116  
M-protein Caffeic acid (1), ferulic acid (1) 112  
NSP 3 Lopinavir (3), chloroquine (1), darunavir (1), eriodictyol (1), pemirolast (1), remdesivir (1), ritonavir (1) 115 and 117  
NSP7-8 Arbidol (1), saquinavir (1) 115 and 116  
NSP9 Conivaptan (1), eriodictyol (1), lopinavir (1), metaquine (1), pemirolast (1), phaitanthrin D (1), saquinavir (1), telmisartan (1) 117, 123 and 135  
Nsp10 Eriodictyol (1), remdesivir (1), saquinavir (1) 116 and 117  
NSP14 Arbidol (1), bictegravir (1), grazoprevir (1), montelukast (1), nilotinib (1), paritaprevir (1), saquinavir (1), simeprevir (1), sinefungin (1) 115, 116 and 133  
NSP15 Lopinavir (1), mefloquine (1), metaquine (1), ritonavir (1), saquinavir (1) 123  
HR1 Glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), ledipasvir (1), paritaprevir (1), simeprevir (1) 131  
Spike–ACE-2 Saquinavir (2), alatrofloxacin (1), batefenterol (1), dovitinib (1), hesperidine (1), lopinavir (1), metaquine (1), oxolinic acid (1), quarfloxin (1), rebamipide (1), rilapladib (1), ritonavir (1) 115, 121 and 123  
ACE 2 Azithromycin (1), brincidofovir (1), burixafor (1), cefmenoxime (1), chloroquine (1), delapril (1), diniprofylline (1), edotecarin (1), eptifibatide (1), ergotamine (1), fluprofylline (1), glycyrrhizic acid (1), hesperidin (1), lisinopril (1), lividomycin (1), lopinavir (1), losartan (1), oseltamivir (1), pemetrexed (1), quisinostat (1), ramipril (1), remdesivir (1), ritonavir (1), silybin (1), spirofylline (1), troglitazone (1), zanamivir (1) 115, 117, 128, 131, 132, 154–157  
TMPRSS2 Ambroxol (1), argatroban (1), benzquercin (1), cefoperazone (1), clindamycin (1), darexaban (1), edoxaban (1), glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), hetacillin (1), icariin (1), ledipasvir (1), letaxaban (1), loprazolam (1), myricitrin (1), nafamostat (1), naringin (1), neohesperidin (1), otamixaban (1), paritaprevir (1), pivampicillin (1), quercitrin (1), rubitecan (1), simeprevir (1) 115, 119, 131, 159–161  
Furin Chloroquine (1), ritonavir (1), sulconazole (1) 129 and 163  
Cathepsin Azithromycin (1), chloroquine (1), hydroxychloroquine (1) 132  
GRP78 Imatinib (1), obinepitide (1), selonsertib (1), zilucoplan (1), 162  
TNF-α Darifenacin (1), ezetimibe (1) 164  
No specific target Abemaciclib (1), adenosine (1), alectinib (1), amorolfine (1), atazanavir (1), boceprevir (1), camphor (1), carvedilol (1), cefmenoxime (1), chlorobutanol (1), chloroquine (1), colchine (1), dacomitinib (1), dactinomycin (1), didanosine (1), d-mannitol (1), dolutegravir (1), d-sorbitol (1), ebastine (1), efavirenz (1), emodine (1), enalaprilat (1), eplerenone (1), equilin (1), flutamide (1), ganciclovir (1), homoharringtonine (1), irbesertan (1), lapatinib (1), lestaurtinib (1), lifitegrast (1), meglumine (1), melatonin (1), mercaptopurine (1), mesalazine (1), nimesulide (1), oxymetholone (1), paroxetine (1) phenazopyridine (1), photofrin (1), pirenzepine (1), promazine (1), quniacrine (1), remdesivir (1), ritonavir (1), salinomycin (1), selimexor (1), sirolimus (1), sodium gluconate (1), thiabendazole (1), tilorone (1), vemurafenib (1), vidarabine (1), vitamin D (1), vorinostat (1) 139–142, 144–146  
Target nameDrugs (frequency)bReferences that report hits
mPRO Saquinavir (14), ritonavir (12), lopinavir (11), nelfinavir (9), indinavir (8), remdesivir (7), simeprevir (7), cobicistat (5), darunavir (5), paritaprevir (5), raltegravir (5), glecaprevir (4), ribavirin (4), amprenavir (3), apixaban (3), disulfiram (3), ledipasvir (3), tipranavir (3), acarbose (2), afatinib (2), atazanavir (2), azelastine (2), azithromycin (2), betrixaban (2), birinapant (2), bromocriptine (2), cangrelor (2), carfilzomib (2), chloroquine (2), cinanserin (2), colistin (2), conivaptan (2), daclatasvir (2), demeclocycline (2), dihydroergotamine (2), dolutegravir (2), doxycycline (2), ebselen (2), edoxaban (2), ergoloid (2), ergotamine (2), grazoprevir (2), hesperidin (2), hydroxychloroquine (2), mefloquine (2), montelukast (2), octreotide (2), oxytetracycline (2), perampanel (2), rivaroxaban (2), shikonin (2), tadalafil (2), telaprevir (2), telmisartan (2), teniposide (2), tetracycline (2), tetrandrine (2), tideglusib (2), tolvaptan (2), valrubicin (2), amodiaquine (1), boceprevir (1), carprofen (1), celecoxib (1), cenicriviroc (1), emetine (1), fluvastatin (1), itraconazole (1), ivermectin (1), proglumetacin (1), sufugolix (1) 26, 27, 29, 31–79, 107–110, 113–123, 125–132, 136–138  
RdRp Remdesivir (7), sofosbuvir (4), galidesivir (3), ribavirin (3), saquinavir (3), cepharanthine (2), colistin (2), dihydroergotamine (2), examorelin (2), favipiravir (2), lypressin (2), novobiocin (2), ornipressin (2), polymyxin b1 (2), simeprevir (2), tenofovir (2), hydroxychloroquine (1) 80, 82–88, 115, 116, 120–122, 126, 127, 129 and 134  
PLproc Ribavirin (3), lopinavir (2), nelfinavir (2), paritaprevir (2), ritonavir (2), cefamandole (1), chloramphenicol (1), chloroquine (1), chlorphenesin (1), darolutamide (1), famotidine (1), glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), ketoamide (1), ledipasvir (1), levodropropizine (1), luteolin (1), phenformin (1), quercetin (1), remdesivir (1), simeprevir (1), thymidine (1), tigecycline (1), tipranavir (1), valganciclovir (1), ziprasidone (1) 90, 91, 113–115, 118, 127, 129 and 131  
N-protein Chloroquine (1), grazoprevir (1), hydroxychloroquine (1), radotinib (1), ribavirin (1), simeprevir (1), valganciclovir (1), zidovudine (1) 92, 93, 112 and 116  
SPIKE Chloroquine (2), glycyrrhizic acid (2), nilotinib (2), azithromycin (1), digitoxin (1), doxycycline (1), lopinavir (1), nelfinavir (1), posaconazole (1), remdesivir (1), ritonavir (1), saquinavir (1) 94–98, 107–110, 115–118, 127, 131, 132, 134 and 135  
NSP1 Cefpiramide (1), cidofovir (1), edoxudine (1), esculin (1), lactose (1), lymecycline (1), phthalocyanine (1), piperacillin (1), remdesivir (1), streptomycin (1), tetracycline (1), tirilazad (1) 99, 100 and 115  
NSP13 Atazanavir (1), bictegravir (1), canrenoic acid (1), cefsulodin (1), dabigatran (1), grazoprevir (1), itraconazole (1), lymecycline (1), meclonazepam (1), oxiphenisatin (1), paritaprevir (1), rolitetracycline (1), simeprevir (1), vapreotide (1) 101, 115, 116, 120 and 133  
NSP15 Arbidol (1), bictegravir (1), dihydroergotamine (1), ergotamine (1), glisoxepide (1), idarubicin (1), nilotinib (1), remdesivir (1), tasosartan (1) 102, 103, 115 and 116  
NSP16 Dolutegravir (2), saquinavir (2), bictegravir (1), cangrelor (1), digitoxin (1), dihydroergotamine (1), eriodictyol (1), favipiravir (1), fludarabine (1), irinotecan (1), maraviroc (1), montelukast (1), prednisolone (1), raltegravir (1), raltgravir (1), ritonavir (1), sinefungin (1), teniposide (1) 104–106, 116, 117 and 130  
NSP10/NSP16 2-deoxyglucose (1), daphnetin (1), methylgallate (1), oxytetracycline (1), telbivudine (1) 125  
E-protein Beclabuvir (1), doxycycline (1), mycophenolic acid (1), rutin (1) 111, 112 and 116  
M-protein Caffeic acid (1), ferulic acid (1) 112  
NSP 3 Lopinavir (3), chloroquine (1), darunavir (1), eriodictyol (1), pemirolast (1), remdesivir (1), ritonavir (1) 115 and 117  
NSP7-8 Arbidol (1), saquinavir (1) 115 and 116  
NSP9 Conivaptan (1), eriodictyol (1), lopinavir (1), metaquine (1), pemirolast (1), phaitanthrin D (1), saquinavir (1), telmisartan (1) 117, 123 and 135  
Nsp10 Eriodictyol (1), remdesivir (1), saquinavir (1) 116 and 117  
NSP14 Arbidol (1), bictegravir (1), grazoprevir (1), montelukast (1), nilotinib (1), paritaprevir (1), saquinavir (1), simeprevir (1), sinefungin (1) 115, 116 and 133  
NSP15 Lopinavir (1), mefloquine (1), metaquine (1), ritonavir (1), saquinavir (1) 123  
HR1 Glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), ledipasvir (1), paritaprevir (1), simeprevir (1) 131  
Spike–ACE-2 Saquinavir (2), alatrofloxacin (1), batefenterol (1), dovitinib (1), hesperidine (1), lopinavir (1), metaquine (1), oxolinic acid (1), quarfloxin (1), rebamipide (1), rilapladib (1), ritonavir (1) 115, 121 and 123  
ACE 2 Azithromycin (1), brincidofovir (1), burixafor (1), cefmenoxime (1), chloroquine (1), delapril (1), diniprofylline (1), edotecarin (1), eptifibatide (1), ergotamine (1), fluprofylline (1), glycyrrhizic acid (1), hesperidin (1), lisinopril (1), lividomycin (1), lopinavir (1), losartan (1), oseltamivir (1), pemetrexed (1), quisinostat (1), ramipril (1), remdesivir (1), ritonavir (1), silybin (1), spirofylline (1), troglitazone (1), zanamivir (1) 115, 117, 128, 131, 132, 154–157  
TMPRSS2 Ambroxol (1), argatroban (1), benzquercin (1), cefoperazone (1), clindamycin (1), darexaban (1), edoxaban (1), glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), hetacillin (1), icariin (1), ledipasvir (1), letaxaban (1), loprazolam (1), myricitrin (1), nafamostat (1), naringin (1), neohesperidin (1), otamixaban (1), paritaprevir (1), pivampicillin (1), quercitrin (1), rubitecan (1), simeprevir (1) 115, 119, 131, 159–161  
Furin Chloroquine (1), ritonavir (1), sulconazole (1) 129 and 163  
Cathepsin Azithromycin (1), chloroquine (1), hydroxychloroquine (1) 132  
GRP78 Imatinib (1), obinepitide (1), selonsertib (1), zilucoplan (1), 162  
TNF-α Darifenacin (1), ezetimibe (1) 164  
No specific target Abemaciclib (1), adenosine (1), alectinib (1), amorolfine (1), atazanavir (1), boceprevir (1), camphor (1), carvedilol (1), cefmenoxime (1), chlorobutanol (1), chloroquine (1), colchine (1), dacomitinib (1), dactinomycin (1), didanosine (1), d-mannitol (1), dolutegravir (1), d-sorbitol (1), ebastine (1), efavirenz (1), emodine (1), enalaprilat (1), eplerenone (1), equilin (1), flutamide (1), ganciclovir (1), homoharringtonine (1), irbesertan (1), lapatinib (1), lestaurtinib (1), lifitegrast (1), meglumine (1), melatonin (1), mercaptopurine (1), mesalazine (1), nimesulide (1), oxymetholone (1), paroxetine (1) phenazopyridine (1), photofrin (1), pirenzepine (1), promazine (1), quniacrine (1), remdesivir (1), ritonavir (1), salinomycin (1), selimexor (1), sirolimus (1), sodium gluconate (1), thiabendazole (1), tilorone (1), vemurafenib (1), vidarabine (1), vitamin D (1), vorinostat (1) 139–142, 144–146  
a

Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.

b

Frequency indicates number of papers which recommend this drug as a promising hit. For Mpro/3CLpro, RdRp, spike and ACE-2 the drugs that have a frequency of 1 and have not been experimentally tested have not been included in the table.

c

No approved drugs were found to inhibit PLpro of SARS-CoV-2.89 

From Table 1.25, it is also clear that there are several viral targets for which specific assays are not available. For some targets, despite availability of assays, all drugs have not been tested. The in silico results provided in this review would help experimentalists to prioritize their screening.

Considering the emergency of SARS-CoV-2 pandemic, significant efforts for drug repurposing using in silico methods have been reported in the literature (approximately 130 published research papers as of 31 August 2020). This chapter summarizes in silico reports on viral as well as human targets associated with COVID-19 and assesses the results based on emerging experimental information. Such comparison with experimental results helps in silico researchers to improve their models and undertake research taking cognizance of new information. The main purpose of these methods is to provide clues to experimentalists in focusing their efforts for specific testing, i.e. which drugs are to be prioritized for testing in a specific target(s) or what is the mechanism of action of a repurposed drug in COVID19? In this endeavor, some reports have succeeded in identifying hits that have been subsequently verified by experiments, whereas in some mechanism of drug was correctly predicted (cf.Table 1.25). It is observed that some studies have not performed any validation of their in silico method(s) prior to screening of compound databases to propose hits. This may lead to reporting of false positives and poor quality of scientific output for drug repurposing.

It is also noted that in spite of available structural information in PDB, very few studies are reported for NSP7, NSP8, NSP9, NSP10 and NSP13. Furthermore, no studies have reported in silico screening for Orf3a, Orf7a, Orf8 and Orf9b despite the availability of their crystal structures. The number of researchers investigating SARS-CoV-2 targets for in silico drug repurposing has been significantly large (∼88%) compared to investigations on human targets involved in COVID-19. A reason for this limited effort could be the possible implications of disrupting normal human processes (e.g. ACE-2 is an important player in the renin–angiotensin system which regulates blood pressure and blood flow to multiple organs, including the lungs, heart and kidneys). The human targets such as TMPRSS2 and furin, known to be involved in direct interaction with SARS-CoV-2, should be investigated further as both the targets are also involved in human diseases such as cancer and metastasis.167–170 

It is also noticed that among in silico methods, structure-based approaches have dominated the literature so far. It is expected that as more target specific experimental information is emerging, efforts using ligand-based and AI approaches will continue to grow and provide robust models for screening of different compound databases.

The promising hits emerging from drug repurposing for SARS-CoV-2 and human targets associated with COVID-19 can further be utilized for development of more active derivatives of these drugs.171,172  Thus, even though some of the drugs have not shown excellent activities against specific targets, they can be used as leads for further new chemical entity development. The Nobel Prize-winning pharmacologist Sir James Black, said, “The most fruitful basis for the discovery of a new drug is to start with an old drug”.

  • Drug repurposing is a way to identify treatment in pandemic such as SARS-CoV-2.

  • This chapter reviews drug repurposing effort for COVID-19 using in silico methods.

  • The review includes in silico work using structure-based, ligand-based and artificial intelligence/network-based methods on both SARS-CoV-2 and human targets.

  • Significant number of papers have been published on SARS-CoV-2 targets (∼88%) compared to human targets.

  • The papers using structure-based methods dominated over ligand-based and other methods.

  • The promising list of drugs for repurposing was provided through in silico methods for experimental testing and multiple papers have identified same drugs as promising hits.

  • Many predictions and hits provided by in silico methods have matched well with the reported experiments.

  • In silico methods have potential to provide drug candidates for experimental screening against specified targets or provide clues on mechanistic aspects of drugs.

Kundan Ingale contributed Sections 1.5–1.8. The remaining sections were contributed by Sudhir Kulkarni.

Authors acknowledge help from several NovaLead Pharma team members, with special thanks to Amol Charya for providing figures.

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