Accelerating multiple medicinal chemistry projects using Artificial Intelligence (AI) : A review from the past 8 years of real world examples.
Alexander G. Dossetter1, Edward Griffen1, Andrew Leach2,1, Shane Montague1, Lauren Reid3, Jessica Stacey4
1MedChemica Ltd
2Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, Merseyside, United Kingdom
3Bioinformatics Institute (A*STAR), 30 Biopolis Street, Matrix, Singapore 138671
4Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, United Kingdom
The technical methods and results of Matched Molecular Pair Analysis (MMPA) applied from a small, individual assay scale through large pharma scale, to multiple pharma data sharing scale have been published and reviewed.1,2,3,4 The drive behind these efforts has been to derive a medicinal chemistry knowledge base (i.e. definitive textbook) that can be applied to drug discovery projects. The aim is to greatly decrease the time in lead identification and optimization by the synthesis of fewer compounds with better properties. Such a system suggests compound designs to expert chemists to triage; such a process is Artificial Intelligence (AI). Given this context, how does this really work on projects? How do the chemists make decisions? What are the results? The talk will answer these questions through multiple project examples where MMPA has been applied and how this led to drug candidates. The projects disclosed are from multiple organisations and describe Cathepsin K inhibitors, Glucokinase Inhibitors, 11 beta-Hydroxysteroid Dehydrogenase Type I Inhibitors (11 beta-HSD1), Ghrelin inverse antagonists and Tubulin Polymerization inhibitors. An overview of MMPA will be presented and each project will be briefly described with a focus on how the chemists used MMPA to understand SAR and design compounds. The impact of project progress to CD will be quantified.
Reference:
1) Dossetter AG, Griffen EJ, Leach AG. Matched Molecular Pair Analysis in drug discovery. Drug Discov Today 2013, 18, 724. doi:10.1016/j.drudis.2013.03.003.
2) Kramer C, Ting A, Zheng H, Hert J, Schindler T, Stahl M, et al. Learning Medicinal Chemistry Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Rules from Cross-Company Matched Molecular Pairs Analysis (MMPA): Miniperspective. J Med Chem 2017. doi:10.1021/acs.jmedchem.7b00935.
3) Lukac I, Zarnecka J, Griffen EJ, Dossetter AG, St-Gallay SA, Enoch SJ, et al. Turbocharging Matched Molecular Pair Analysis: Optimizing the Identification and Analysis of Pairs. J Chem Inf Model 2017, 57, 2424. doi:10.1021/acs.jcim.7b00335.
4) Griffen EJ, Dossetter AG, Leach AG, Montague S. Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence? Drug Discov Today 2013, 18, 724 doi:10.1016/j.drudis.2013.03.003.
