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Objectives. The main purpose of this work is to adapt the architecture of the REINVENT neural network to generate potential inhibitors of the HIV-1 envelope protein gp120 using in the learning process with reinforcement of molecular docking on GPUs.Methods. To modify the initial network model, molecular docking on GPUs implemented in the learning process with reinforcement was used, and an algorithm was developed that allows converting the representations of connections generated by the SMILES network into the PDBQT format necessary for docking. To accelerate the learning of the neural network in the modified version of the REINVENT model, the AutoDock-Vina-GPU-2.1 docking program was used, and to clarify the results of its work, the procedure for revaluing the affinity of compounds to the target using the RFScore-4 evaluation function was used.Results. Using a modified version of the REINVENT model, more than 60,000 compounds were obtained, of which about 52,000 molecules have a binding energy value to the HIV-1 gp120 protein comparable to the value calculated for the HIV-1 inhibitor NBD-14204, used in calculations as a positive control. Of the 52,000 compounds selected, about 34,000 molecules satisfy the restrictions imposed on a potential drug to ensure its bioavailability when taken orally.Conclusion. The results obtained allow us to demonstrate the effectiveness of an adapted neural network by the example of designing new potential inhibitors of the gp120 HIV-1 protein capable of blocking the CD4- binding site of the gp120 virus envelope protein and preventing its penetration into host cells.

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.