Artificial Intelligence in Drug Discovery
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.
Artificial Intelligence in Drug Discovery, The Royal Society of Chemistry, 2020.
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Table of contents
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Chapter 2: The History of Artificial Intelligence and Chemistryp7-14ByNathan BrownNathan BrownSearch for other works by this author on:
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Chapter 3: Chemical Topic Modeling – An Unsupervised Approach Originating from Text-mining to Organize Chemical Datap15-44ByNadine Schneider;Nadine SchneiderNovartis Institutes for Biomedical Research, Global Discovery Chemistry/CADD, Novartis Pharma AGCH-4002 BaselSwitzerland[email protected]Search for other works by this author on:Nikolas Fechner;Nikolas FechnerNovartis Institutes for Biomedical Research, Informatics/Chemistry Information Systems, Novartis Pharma AGCH-4002 BaselSwitzerlandSearch for other works by this author on:Nikolaus Stiefl;Nikolaus StieflNovartis Institutes for Biomedical Research, Global Discovery Chemistry/CADD, Novartis Pharma AGCH-4002 BaselSwitzerland[email protected]Search for other works by this author on:Gregory A. LandrumGregory A. LandrumKNIME AGHardturmstr 668005 ZurichSwitzerlandSearch for other works by this author on:
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Chapter 4: Deep Learning and Chemical Datap45-62ByJohn BoyleJohn BoyleSearch for other works by this author on:
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Chapter 5: Concepts and Applications of Conformal Prediction in Computational Drug Discoveryp63-101ByIsidro Cortés-Ciriano;Isidro Cortés-CirianoEuropean Molecular Biology Laboratory, European Bioinformatics InstituteWellcome Genome CampusHinxton CB10 1SDUK[email protected]Search for other works by this author on:Andreas BenderAndreas BenderCentre for Molecular Informatics, Department of Chemistry, University of CambridgeLensfield RoadCambridgeCB2 1EWUK[email protected]Search for other works by this author on:
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Chapter 6: Non-applicability Domain. The Benefits of Defining “I Don't Know” in Artificial Intelligencep102-118ByDamjan KrstajicDamjan KrstajicSearch for other works by this author on:
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Chapter 7: Predicting Protein-ligand Binding Affinitiesp119-150ByJosé Jiménez-Luna;José Jiménez-LunaComputational Science Laboratory, Universitat Pompeu Fabra, PRBBC Dr Aiguader 8808003BarcelonaSpain[email protected]Search for other works by this author on:Gianni De FabritiisGianni De FabritiisComputational Science Laboratory, Universitat Pompeu Fabra, PRBBC Dr Aiguader 8808003BarcelonaSpain[email protected]ICREAPasseig Lluis Companys 2308010 BarcelonaSpainSearch for other works by this author on:
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Chapter 8: Virtual Screening with Convolutional Neural Networksp151-183ByFergus Imrie;Fergus ImrieOxford Protein Informatics Group, Department of Statistics, University of OxfordOxfordOX1 3LBUK[email protected]Search for other works by this author on:Anthony R. Bradley;Anthony R. BradleyExscientia Ltd36 St. Giles’OxfordOX1 3LDUKSearch for other works by this author on:Charlotte M. DeaneCharlotte M. DeaneOxford Protein Informatics Group, Department of Statistics, University of OxfordOxfordOX1 3LBUK[email protected]Search for other works by this author on:
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Chapter 9: Machine Learning in the Area of Molecular Dynamics Simulationsp184-214ByShuzhe Wang;Shuzhe WangDepartment of Chemistry and Applied Biosciences, ETH ZürichVladimir-Prelog-Weg 28093 ZürichSwitzerland[email protected]Search for other works by this author on:Sereina RinikerSereina RinikerDepartment of Chemistry and Applied Biosciences, ETH ZürichVladimir-Prelog-Weg 28093 ZürichSwitzerland[email protected]Search for other works by this author on:
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Chapter 10: Compound Design Using Generative Neural Networksp215-227ByT. Blaschke;T. BlaschkeLife Science Informatics, B-IT, University of BonnEndenicher Allee 19cD-53115 BonnGermany[email protected]Search for other works by this author on:J. BajorathJ. BajorathLife Science Informatics, B-IT, University of BonnEndenicher Allee 19cD-53115 BonnGermany[email protected]Search for other works by this author on:
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Chapter 11: Junction Tree Variational Autoencoder for Molecular Graph Generationp228-249ByWengong Jin;Wengong JinMIT Computer Science & Artificial Intelligence Laboratory9A Academy StArlingtonMA02476USA[email protected]Search for other works by this author on:Regina Barzilay;Regina BarzilayMIT Computer Science & Artificial Intelligence Laboratory9A Academy StArlingtonMA02476USA[email protected]Search for other works by this author on:Tommi JaakkolaTommi JaakkolaMIT Computer Science & Artificial Intelligence Laboratory9A Academy StArlingtonMA02476USA[email protected]Search for other works by this author on:
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Chapter 12: AI via Matched Molecular Pair Analysisp250-271ByAndrew G. LeachAndrew G. LeachSearch for other works by this author on:
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Chapter 13: Molecular De Novo Design Through Deep Generative Modelsp272-300ByOla Engkvist;Ola EngkvistHit Discovery, Discovery Sciences, R&D BiopharmaceuticalsAstraZeneca GothenburgSweden[email protected]Search for other works by this author on:Josep Arús-Pous;Josep Arús-PousHit Discovery, Discovery Sciences, R&D BiopharmaceuticalsAstraZeneca GothenburgSweden[email protected]Search for other works by this author on:Esben Jannik Bjerrum;Esben Jannik BjerrumHit Discovery, Discovery Sciences, R&D BiopharmaceuticalsAstraZeneca GothenburgSweden[email protected]Search for other works by this author on:Hongming ChenHongming ChenHit Discovery, Discovery Sciences, R&D BiopharmaceuticalsAstraZeneca GothenburgSweden[email protected]Search for other works by this author on:
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Chapter 14: Active Learning for Drug Discovery and Automated Data Curationp301-326ByD. RekerD. RekerSearch for other works by this author on:
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Chapter 15: Data-driven Prediction of Organic Reaction Outcomesp327-348ByConnor W. ColeyConnor W. ColeyDepartment of Chemical Engineering, Massachusetts Institute of TechnologyCambridgeMA 02139USA[email protected]Search for other works by this author on:
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Chapter 16: ChemOS: An Orchestration Software to Democratize Autonomous Discoveryp349-388ByLoïc M. Roch;Loïc M. RochDepartment of Chemistry, University of Toronto80 St George StTorontoON M5S 3H6Canada[email protected]Department of Computer Science, University of Toronto214 College StTorontoON M5T 3A1CanadaVector Institute for Artificial Intelligence661 University Ave Suite 710TorontoON M5G 1M1CanadaDepartment of Chemistry and Chemical Biology, Harvard UniversityCambridgeMA 02138USASearch for other works by this author on:Florian Häse;Florian HäseDepartment of Chemistry, University of Toronto80 St George StTorontoON M5S 3H6Canada[email protected]Department of Computer Science, University of Toronto214 College StTorontoON M5T 3A1CanadaVector Institute for Artificial Intelligence661 University Ave Suite 710TorontoON M5G 1M1CanadaDepartment of Chemistry and Chemical Biology, Harvard UniversityCambridgeMA 02138USASearch for other works by this author on:Alán Aspuru-GuzikAlán Aspuru-GuzikDepartment of Chemistry, University of Toronto80 St George StTorontoON M5S 3H6Canada[email protected]Department of Computer Science, University of Toronto214 College StTorontoON M5T 3A1CanadaVector Institute for Artificial Intelligence661 University Ave Suite 710TorontoON M5G 1M1CanadaDepartment of Chemistry and Chemical Biology, Harvard UniversityCambridgeMA 02138USALebovic Fellow, Canadian Institute for Advanced Research (CIFAR)TorontoON M5G 1M1CanadaSearch for other works by this author on:
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Chapter 17: Summary and Outlookp389-393ByNathan BrownNathan BrownSearch for other works by this author on:
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