In Silico Estimation of the Safety of Pharmacologically Active Substances Using Machine Learning Methods: A Review - V. V. Poroikov; A. V. Dmitriev; D. S. Druzhilovskiy; S. M. Ivanov; A. A. Lagunin; P. V. Pogodin; A. V. Rudik; P. I. Savosina; O. A. Tarasova; D. A. Filimonov (Ministry of Health of the Russian Federation, Federal State Budgetary Institution «Scientific Centre for Expert Evaluation of Medicinal Products», 2023)
Scientific relevance. Currently, machine learning (ML) methods are widely used in the research and development of new pharmaceuticals. ML methods are particularly important for assessing the safety of pharmacologically active substances early in the research process because such safety assessments significantly reduce the risk of obtaining negative results in the future.Aim. This study aimed to review the main information and prediction resources that can be used for the assessment of the safety of pharmacologically active substances in silico.Discussion. Novel ML methods can identify the most likely molecular targets for a specific compound to interact with, based on structure-activity relationship analysis. In addition, ML methods can be used to search for potential therapeutic and adverse effects, as well as to study acute and specific toxicity, metabolism, and other pharmacodynamic, pharmacokinetic, and toxicological characteristics of investigational substances. Obtained at early stages of research, this information helps to prioritise areas for experimental testing of biological activity, as well as to identify compounds with a low probability of producing adverse and toxic effects. This review describes free online ML-based information and prediction resources for assessing the safety of pharmacologically active substances using their structural formulas. Special attention is paid to the Russian computational products presented on the Way2Drug platform (https://www.way2drug.com/dr/).Conclusions. Contemporary approaches to the assessment of pharmacologically active substances in silico based on structure-activity relationship analysis using ML methods provide information about various safety characteristics and allow developers to select the most promising candidates for further in-depth preclinical and clinical studies.