Skip to Main Content
Skip Nav Destination

With the advances in technology and the vast datastreams now produced by modern analytical instrumentation, computational models, systems and routines have been developed to better treat the information obtained. Indeed, we have seen such advances in applications of AI techniques to chemical data science that certainly the relevant chapter required updating multiple times throughout the publication process.

We have recruited a comprehensive group of colleagues internationally to develop this monograph. With chapters ranging from fundamental simulations of nuclear quantum effects, through to overviews of the various spectroscopic methods available and the computational techniques applied therein, this volume should appeal to colleagues both in the chemical and biosciences as a broad and comprehensive overview of in silico applications in analytical methodologies.

We begin in Chapter 1 with an overview of computational techniques as applied to metabolomic data analysis. Therein, Percival, Grootveld, and colleagues describe the significant advances over the past decade in data treatment, curation and availability, whilst describing well-known tools such as Wishart's Metaboanalyst packages.

Chapter 2, co-authored by Castro and Swart, consists of a broad and timely review of computational NMR spectroscopy for predictive purposes. The reproduction of NMR experiments in simulations is considered, as well as the numerous parameters contributing to spectral similarity with experiment.

In Chapter 3, Dario Estrin and colleagues from Universidad de Buenos Aires provide a comprehensive and contemporary perspective on computational vibrational spectroscopy. Indeed, they describe the state of the art within the field, covering the most recent developments in the context of the levels of theory available and applied. Discussions of (an)harmonicity complement the narrative which covers classical to fully QM approaches.

Paneth and Dybala-Defratyka consider the use of isotope effects, nuclear quantum effects and analytical probes in Chapter 4. They provide an overview of predicting isotope effects (both kinetic and equilibrium), and the information obtained which can be employed to consider the behaviour of a variety of chemical and biological systems. Applications of theoretical estimations of isotope effects are described, such as through reactions in solution, and the employment of a range of QM/MM procedures are considered in the context of enzyme-catalysed reactions.

Applications of artificial intelligence techniques in chemical and biochemical analysis are discussed in Chapter 5, where Gibson, Kamerlin, Wilson and colleagues first provide a holistic overview of theory and history in the field, prior to covering a snapshot of advances, particularly over the last 5 years. The developments by groups such as those of Aspuru-Guzik, Cronin and others in bringing AI techniques to the forefront of contemporary chemical analysis are described, whilst a perspective for future applications is offered.

Monari, Francés-Monerris, and co-workers explain the development of computational spectroscopic approaches for complex biological systems in Chapter 6. First offering a short review of hybrid QM/MM techniques, they continue to provide vital guidance in the simulation of photobiological phenomena, including the adequate sampling of chromophore conformations. The narrative develops the theory and application of computational techniques in circular dichroism spectra, and a discussion on modern methods for understanding the evolution of excited states.

In Chapter 7, Lucie Delemotte from KTH Stockholm considers bridging the gap between atomistic MD simulations and laboratory experiments for investigations of membrane proteins. The link between experimental characterisation and simulation is established, as alongside a consideration of the theoretical models developed for MD and recent advances. Moreover, a useful discussion is developed, considering the degree of automation of such studies, as well as the necessary comparability to interpretable experimental data.

Chapter 8, by Molinari and colleagues, provides a perspective of computational chemical analysis as applied to solid state chemistry, in particular, materials science. The methodologies and protocols applied to materials analysis are surveyed, considering surfaces and their interactions with the environment. Comparative assessments of computational methods and experimental techniques are offered, suggesting the substantial advantages combinatorial approaches have on contemporary studies.

We conclude by considering the computational approaches inherent in the analysis of ESR spectral data in Chapter 9, where Martín, Mather, Wilson and co-workers approach the topic pedagogically through a review of fundamental ESR theory, prior to describing recent developments in spectral analysis codes and algorithms.

Whilst we have collated a heterogenous collection of contributions, we hope readers will agree that these span the scope of computational approaches in analytical chemical and biosciences, and we are grateful to the Royal Society of Chemistry for co-creating this initiative.

Philippe B. Wilson and Martin Grootveld

Close Modal

or Create an Account

Close Modal
Close Modal