Computational chemistry is a term that encompasses a wide variety of approaches and strategies used to advance the science involved in a range of disciplines around drug discovery and development. Perhaps paradoxically, virtual screening is time- and resource-consuming and mass screening of libraries is still more efficiently achieved through a wet chemistry approach. The advantages of a virtual approach are myriad in the drug discovery process and some of the major advantages of a computational approach is to examine previously unexplored chemical space and to predict promising structures and arrive at a lead structure more rapidly than would be expected from standard structure activity relationship studies. So, computational chemistry has moved on from just similarity searching, to a technology that has the ability to learn and assist in data mining studies. Thus computational chemistry has evolved over the past 20 years 

The free downloads available in this newsletter highlight some of the most recent developments in computational chemistry and explore some of the diverse areas that this approach is impacting. I’ll give a short outline of the importance of these free downloads.  

The first article, by Daniel Reker and Gisbert Schneider of The Swiss Federal Institute of Technology, entitled: “Active-learning strategies in computer-assisted drug discovery” overviews how active-learning systems can assist the chemist in the selection of the most appropriate structure from examination of underexplored chemical space, whilst producing chemical novelty from such approaches. Some of the advantages from an active-learning approach are that there is potentially significant savings to be made and can reduce the usage of precious reagents. 

The second article, from Antonio Lavecchia of The Department of Pharmacy, Drug Discovery Laboratory, University of Napoli entitled: “Machine-learning approaches in drug discovery: methods and applications” expands some of the advantages and approaches addressed in the first article. Dr. Lavecchia concentrates more upon the application of machine-learning approaches to virtual screening. The review compares and contrasts a number of recently published studies and represents a state-of-the-art overview of the field. It gives a “warts and all” view of not only the benefits, but also the issues and difficulties of incorporating such approaches into a drug discovery framework. 

Finally, is the review from Ákos Tarcsay and György M. Keserü of  Budapest, Hungary, entitled, “Is there a link between selectivity and binding thermodynamics profiles?”.  The authors discuss how the thermodynamics of ligand binding is strongly influenced by the enthalpy and entropy of ligand-target interaction. Using publically-available data using a wide range of target and off-target binding, the authors examine the relationships between selectivity and binding thermodynamics. In short, they conclude that ligands whose binding to their target has a strong entropic contribution hit more off-targets that those whose binding is more strongly enthalpic.  

Steve Carney was born in Liverpool, England and studied Biochemistry at Liverpool University, obtaining a BSc.(Hons) and then read for a PhD on the Biochemistry and Pathology of Connective Tissue Diseases in Manchester University, in the Departments of Medical Biochemistry and Histopathology. On completion of his PhD he moved to the Kennedy Institute of Rheumatology, London, where he worked with Professor Helen Muir FRS and Professor Tim Hardingham, on the biochemistry of experimental Osteoarthritis. He joined Eli Lilly and Co. and held a number of positions in Biology R&D, initially in the Connective Tissue Department, but latterly in the Neuroscience Department. He left Lilly to take up his present position as Managing Editor, Drug Discovery Today, at Elsevier. Currently, he also holds an honorary lectureship in Drug Discovery at the University of Surrey, UK. He has authored over 40 peer-reviewed articles, written several book chapters and has held a number of patents. No change this month in Steve or his band’s rankings. Unlikely to change until September.