safetyriskБезопасность и риск фармакотерапииSafety and Risk of Pharmacotherapy2312-78212619-1164Federal State Budgetary Institution ‘Scientific Centre for Expert Evaluation of Medicinal Products’ of the Ministry of Health of the Russian Federation (FSBI ‘SCEEMP’)10.30895/2312-7821-2023-11-4-372-389safetyrisk-397Research ArticleГЛАВНАЯ ТЕМА: ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ЗДРАВООХРАНЕНИИ: ПОЛЬЗА И РИСКИMAIN TOPIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE: RISKS AND BENEFITSОценка безопасности фармакологических веществ in silico с применением методов машинного обучения: обзорIn Silico Estimation of the Safety of Pharmacologically Active Substances Using Machine Learning Methods: A Reviewhttps://orcid.org/0000-0001-7937-2621ПоройковВ. В.PoroikovV. V.

Поройков Владимир Васильевич, член-корреспондент РАН, профессор, д-р биол. наук, канд. физ.-мат. наук

119121, Москва, ул. Погодинская, д.10, стр. 8

Vladimir V. Poroikov, Corresponding Member of the Russian Academician of Science (RAS), Professor, Dr. Sci. (Biol.), Cand. Sci. (Phys.-Math.)

10/8 Pogodinskaya St., Moscow 119121

vladimir.poroikov@ibmc.msk.ruhttps://orcid.org/0000-0002-2431-3429ДмитриевА. В.DmitrievA. V.

Дмитриев Александр Викторович, канд. биол. наук

119121, Москва, ул. Погодинская, д.10, стр. 8

Alexander V. Dmitriev, Cand. Sci. (Biol.)

10/8 Pogodinskaya St., Moscow 119121

a.v.dmitriev@mail.ruhttps://orcid.org/0000-0001-9024-1331ДружиловскийД. С.DruzhilovskiyD. S.

Дружиловский Дмитрий Сергеевич, канд. биол. наук 

119121, Москва, ул. Погодинская, д.10, стр. 8

Dmitry S. Druzhilovskiy, Cand. Sci. (Biol.)

10/8 Pogodinskaya St., Moscow 119121

dmitry.druzhilovsky@ibmc.msk.ruhttps://orcid.org/0000-0002-3177-6237ИвановС. М.IvanovS. M.

Иванов Сергей Михайлович, канд. биол. наук

119121, Москва, ул. Погодинская, д.10, стр. 8

ул. Островитянова, д. 1, Москва, 117997

Sergey M. Ivanov, Cand. Sci. (Biol.)

10/8 Pogodinskaya St., Moscow 119121

1 Ostrovityanov St., Moscow 117997

smivanov7@gmail.comhttps://orcid.org/0000-0003-1757-8004ЛагунинА. А.LaguninA. A.

Лагунин Алексей Александрович, д-р биол. наук, профессор РАН

119121, Москва, ул. Погодинская, д.10, стр. 8

ул. Островитянова, д. 1, Москва, 117997

Alexey A. Lagunin, Dr. Sci. (Biol.), Professor of the Russian Academy of Sciences (RAS) 

10/8 Pogodinskaya St., Moscow 119121

1 Ostrovityanov St., Moscow 117997

alexey.lagunin@ibmc.msk.ruhttps://orcid.org/0000-0003-1843-7668ПогодинП. В.PogodinP. V.

Погодин Павел Викторович, канд. биол. наук

119121, Москва, ул. Погодинская, д.10, стр. 8

Pavel V. Pogodin, Cand. Sci. (Biol.) 

10/8 Pogodinskaya St., Moscow 119121

pogodinpv@gmail.comhttps://orcid.org/0000-0002-8916-9675РудикА. В.RudikA. V.

Рудик Анастасия Владимировна, канд. биол. наук

119121, Москва, ул. Погодинская, д.10, стр. 8

Anastasiya V. Rudik, Cand. Sci. (Biol.)

10/8 Pogodinskaya St., Moscow 119121

rudik_anastassia@mail.ruhttps://orcid.org/0000-0001-7066-7925СавосинаП. И.SavosinaP. I.

Савосина Полина Игоревна

119121, Москва, ул. Погодинская, д.10, стр. 8

Polina I. Savosina

10/8 Pogodinskaya St., Moscow 119121

polina.savosina@ibmc.msk.ruhttps://orcid.org/0000-0002-3723-7832ТарасоваО. А.TarasovaO.  A.

Тарасова Ольга Александровна, канд. биол. наук

119121, Москва, ул. Погодинская, д.10, стр. 8

Olga A. Tarasova, Cand. Sci. (Biol.) 

10/8 Pogodinskaya St., Moscow 119121

fioland@yandex.ruhttps://orcid.org/0000-0002-0339-8478ФилимоновД. А.FilimonovD. A.

Филимонов Дмитрий Алексеевич, канд. физ.-мат. наук

119121, Москва, ул. Погодинская, д.10, стр. 8

Dmitry A. Filimonov, Cand. Sci. (Phys.-Math.) 

10/8 Pogodinskaya St., Moscow 119121

dmitry.filimonov@ibmc.msk.ruФедеральное государственное бюджетное научное учреждение "Научно-исследовательский институт биомедицинской химии имени В.Н. Ореховича"РоссияV.N. Orekhovich Research Institute of Biomedical ChemistryRussian FederationФедеральное государственное бюджетное научное учреждение "Научно-исследовательский институт биомедицинской химии имени В.Н. Ореховича"; Федеральное государственное автономное образовательное учреждение высшего образования «Российский национальный исследовательский медицинский университет им. Н.И. Пирогова» Министерства здравоохранения Российской ФедерацииРоссияV.N. Orekhovich Research Institute of Biomedical Chemistry; N.I. Pirogov Russian National Research Medical UniversityRussian Federation202313112023114372389Copyright © Поройков В.В., Дмитриев А.В., Дружиловский Д.С., Иванов С.М., Лагунин А.А., Погодин П.В., Рудик А.В., Савосина П.И., Тарасова О.А., Филимонов Д.А., 20232023Поройков В.В., Дмитриев А.В., Дружиловский Д.С., Иванов С.М., Лагунин А.А., Погодин П.В., Рудик А.В., Савосина П.И., Тарасова О.А., Филимонов Д.А.Poroikov V.V., Dmitriev A.V., Druzhilovskiy D.S., Ivanov S.M., Lagunin A.A., Pogodin P.V., Rudik A.V., Savosina P.I., Tarasova O.A., Filimonov D.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.risksafety.ru/jour/article/view/397Актуальность

Актуальность. В исследованиях и разработках новых лекарственных препаратов повсеместно используются методы машинного обучения (ML). Их применение особенно актуально для оценки безопасности фармакологически активных веществ на ранних стадиях исследований, что существенно снижает риски получения отрицательных результатов в дальнейшем.

Цель

Цель. Обзор основных информационных и прогностических ресурсов, которые могут быть использованы для оценки безопасности фармакологически активных веществ с применением методов in silico.

Обсуждение

Обсуждение. Использование новых методов ML позволяет на основе анализа зависимостей «структура– активность» оценивать наиболее вероятные молекулярные мишени, с которыми может взаимодействовать конкретное соединение, потенциальные фармакотерапевтические и побочные эффекты, острую и специфическую токсичность, метаболизм и другие фармакодинамические, фармакокинетические и токсикологические характеристики изучаемых веществ. Получение этой информации дает возможность определить приоритетные направления экспериментального тестирования биологической активности на ранних стадиях исследований и отобрать соединения с низкой вероятностью проявления побочных и токсических эффектов. В настоящем обзоре рассмотрены свободно доступные в сети Интернет информационные и прогностические ресурсы, которые позволяют оценивать безопасность соединения на основе его структурной формулы с применением подходов ML. Особое внимание уделено отечественным компьютерным разработкам, представленным на платформе Way2Drug (https://www.way2drug.com/dr/).

Выводы

Выводы. Современные методы компьютерной оценки свойств фармакологически активных веществ на основе анализа зависимостей «структура–активность» методами ML обеспечивают получение информации о различных характеристиках безопасности этих соединений и позволяют отбирать наиболее перспективные кандидаты для углубленных доклинических и клинических исследований.

Scientific relevance

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

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

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

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.

фармакологически активные веществабезопасностьисследования in silicoанализ зависимость «структура–активность»компьютерное конструирование лекарственных средствмашинное обучениеплатформа Way2Drugpharmacologically active substancessafetyin silico studiesstructure–activity relationshipSARcomputer-aided drug designmachine learningWay2DrugРабота выполнена в рамках гранта Российского научного фонда, проект № 19-15-00396, <a href="https://rscf.ru/project/19-15-00396/">https://rscf.ru/project/19-15-00396/</a>This study was carried out within the framework of Russian Science Foundation project No. 19-15-00396, <a href="https://rscf.ru/project/19-15-00396/">https://rscf.ru/project/19-15-00396/</a>.ReferencesJorgensen WL. The many roles of computation in drug discovery. Science. 2004;303(5665):1813–8. https://doi.org/10.1126/science.1096361Jorgensen WL. The many roles of computation in drug discovery. Science. 2004;303(5665):1813–8. https://doi.org/10.1126/science.1096361Беженцев ВМ, Дружиловский ДС, Иванов СМ, Филимонов ДА, Sastry GN, Поройков ВВ. Веб-ресурсы для поиска и разработки новых лекарственных препаратов. Химико-фармацевтический журнал. 2017;51(2):3–11. https://doi.org/10.30906/0023-1134-2017-51-2-3-11Bezhentsev VM, Druzhilovskii DS, Ivanov SM, Filimonov DA, Sastry GN, Poroikov VV. Web resources for discovery and development of new medicines. Pharm Chem J. 2017;51(2):91–9. https://doi.org/10.1007/s11094-017-1563-xBajorath J, Overington J, Jenkins JL, Walters P. Drug discovery and development in the era of Big Data. Future Med Chem. 2016;8(15):1807–13. https://doi.org/10.4155/fmc-2014-0081Bajorath J, Overington J, Jenkins JL, Walters P. Drug discovery and development in the era of Big Data. Future Med Chem. 2016;8(15):1807–13. https://doi.org/10.4155/fmc-2014-0081Fourches D, Muratov E, Tropsha A. Trust, but verify II: a practical guide to chemogenomics data curation. J Chem Inf Model. 2016;56(7):1243–52. https://doi.org/10.1021/acs.jcim.6b00129Fourches D, Muratov E, Tropsha A. Trust, but verify II: a practical guide to chemogenomics data curation. J Chem Inf Model. 2016;56(7):1243–52. https://doi.org/10.1021/acs.jcim.6b00129Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, et al. QSAR without borders. Chem Soc Rev. 2020;49(11):3525–64. https://doi.org/10.1039/D0CS00098AMuratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, et al. QSAR without borders. Chem Soc Rev. 2020;49(11):3525–64. https://doi.org/10.1039/D0CS00098APun FW, Ozerov IV, Zhavoronkov A. AI-powered therapeutic target discovery. Trends Pharmacol Sci. 2023;44(9):561–72. https://doi.org/10.1016/j.tips.2023.06.010Pun FW, Ozerov IV, Zhavoronkov A. AI-powered therapeutic target discovery. Trends Pharmacol Sci. 2023;44(9):561–72. https://doi.org/10.1016/j.tips.2023.06.010Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discov Today. 2021;26(2):511–24. https://doi.org/10.1016/j.drudis.2020.12.009Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discov Today. 2021;26(2):511–24. https://doi.org/10.1016/j.drudis.2020.12.009Bender A, Cortes-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov Today. 2021;26(4):1040–52. https://doi.org/10.1016/j.drudis.2020.11.037Bender A, Cortes-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov Today. 2021;26(4):1040–52. https://doi.org/10.1016/j.drudis.2020.11.037Hasselgren C, Oprea TI. Artificial intelligence for drug discovery: are we there yet? Annu Rev Pharmacol Toxicol. 2024;64:12023. https://doi.org/10.1146/annurev-pharmtox-040 323-040828Hasselgren C, Oprea TI. Artificial intelligence for drug discovery: are we there yet? Annu Rev Pharmacol Toxicol. 2024;64:12023. https://doi.org/10.1146/annurev-pharmtox-040 323-040828Wadman M. FDA no longer has to require animal testing for new drugs. Science. 2023;379(6628):127–8. https://doi.org/10.1126/science.adg6276Wadman M. FDA no longer has to require animal testing for new drugs. Science. 2023;379(6628):127–8. https://doi.org/10.1126/science.adg6276Luo M, Wang XS, Tropsha A. Comparative analysis of QSAR-based vs. chemical similarity based predictors of GPCRs binding affinity. Mol Inform. 2016;35(1):36–41. https://doi.org/10.1002/minf.201500038Luo M, Wang XS, Tropsha A. Comparative analysis of QSAR-based vs. chemical similarity based predictors of GPCRs binding affinity. Mol Inform. 2016;35(1):36–41. https://doi.org/10.1002/minf.201500038Murtazalieva KA, Druzhilovskiy DS, Goel RK, Sastry GN, Poroikov VV. How good are publicly available web services that predict bioactivity profiles for drug repurposing? SAR QSAR Environ Res. 2017;28(10):843–62. https://doi.org/10.1080/1062936X.2017.1399448Murtazalieva KA, Druzhilovskiy DS, Goel RK, Sastry GN, Poroikov VV. How good are publicly available web services that predict bioactivity profiles for drug repurposing? SAR QSAR Environ Res. 2017;28(10):843–62. https://doi.org/10.1080/1062936X.2017.1399448Forouzesh A, Samadi Foroushani S, Forouzesh F, Zand E. Reliable target prediction of bioactive molecules based on chemical similarity without employing statistical methods. Front Pharmacol. 2019;10:835. https://doi.org/10.3389/fphar.2019.00835Forouzesh A, Samadi Foroushani S, Forouzesh F, Zand E. Reliable target prediction of bioactive molecules based on chemical similarity without employing statistical methods. Front Pharmacol. 2019;10:835. https://doi.org/10.3389/fphar.2019.00835Ji KY, Liu C, Liu ZQ, Deng YF, Hou TJ, Cao DS. Comprehensive assessment of nine target prediction web services: which should we choose for target fishing? Brief Bioinform. 2023;24(2):bbad014. https://doi.org/10.1093/bib/bbad014Ji KY, Liu C, Liu ZQ, Deng YF, Hou TJ, Cao DS. Comprehensive assessment of nine target prediction web services: which should we choose for target fishing? Brief Bioinform. 2023;24(2):bbad014. https://doi.org/10.1093/bib/bbad014Буров ЮВ, Корольченко ЛВ, Поройков ВВ. Государственная система регистрации и биологических испытаний химических соединений: возможности для изыскания новых лекарственных препаратов. Бюллетень Всесоюзного научного центра по безопасности биологически активных веществ. 1990;(1):4–25.Burov YuV, Korolchenko LV, Poroikov VV. State system of registration and biological testing of chemical compounds: opportunities for finding new drugs. Bulletin of the All-Union Scientific Center for the Safety of Biologically Active Substances. 1990;(1):4–25 (In Russ.).Lagunin A, Stepanchikova A, Filimonov D, Poroikov V. PASS: prediction of activity spectra for biologically active substances. Bioinformatics. 2000;16(8):747–8. https://doi.org/10.1093/bioinformatics/16.8.747Lagunin A, Stepanchikova A, Filimonov D, Poroikov V. PASS: prediction of activity spectra for biologically active substances. Bioinformatics. 2000;16(8):747–8. https://doi.org/10.1093/bioinformatics/16.8.747Филимонов ДА, Дружиловский ДС, Лагунин АА, Глориозова ТА, Рудик АВ, Дмитриев АВ и др. Компьютерное прогнозирование спектров биологической активности химических соединений: возможности и ограничения. Biomedical Chemistry: Research and Methods. 2018;1(1):e00004. https://doi.org/10.18097/bmcrm00004Filimonov DA, Druzhilovskiy DS, Lagunin AA, Gloriozova TA, Rudik AV, Dmitriev AV, et al. Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitations. Biomedical Chemistry: Research and Methods. 2018;1(1):e00004 (In Russ.). https://doi.org/10.18097/bmcrm00004Filimonov DA, Zakharov AV, Lagunin AA, Poroikov VV. QNA based “Star Track” QSAR approach. SAR QSAR Environ Res. 2009;20(7–8):679–709. https://doi.org/10.1080/10629360903438370Filimonov DA, Zakharov AV, Lagunin AA, Poroikov VV. QNA based “Star Track” QSAR approach. SAR QSAR Environ Res. 2009;20(7–8):679–709. https://doi.org/10.1080/10629360903438370Lagunin A, Zakharov A, Filimonov D, Poroikov V. QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol Inform. 2011;30 (2–3);241–50. https://doi.org/10.1002/minf.201000151Lagunin A, Zakharov A, Filimonov D, Poroikov V. QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol Inform. 2011;30 (2–3);241–50. https://doi.org/10.1002/minf.201000151Stolbov LA, Filimonov DA, Poroikov VV. SAR based on self-consistent classifier. SAR QSAR Environ Res. 2022;33(10):793–804. https://doi.org/10.1080/1062936X.2022.2139751Stolbov LA, Filimonov DA, Poroikov VV. SAR based on self-consistent classifier. SAR QSAR Environ Res. 2022;33(10):793–804. https://doi.org/10.1080/1062936X.2022.2139751Sakamuru S, Huang R, Xia M. Use of Tox21 screening data to evaluate the COVID-19 drug candidates for their potential toxic effects and related pathways. Front Pharmacol. 2022;13:935399. https://doi.org/10.3389/fphar.2022.935399Sakamuru S, Huang R, Xia M. Use of Tox21 screening data to evaluate the COVID-19 drug candidates for their potential toxic effects and related pathways. Front Pharmacol. 2022;13:935399. https://doi.org/10.3389/fphar.2022.935399Pogodin PV, Lagunin AA, Filimonov DA, Poroikov VV. PASS Targets: ligand-based multi-target computational system based on public data and Naïve Bayes approach. SAR QSAR Environ Res. 2015;26(10):783–93. https://doi.org/10.1080/1062936X.2015.1078407Pogodin PV, Lagunin AA, Filimonov DA, Poroikov VV. PASS Targets: ligand-based multi-target computational system based on public data and Naïve Bayes approach. SAR QSAR Environ Res. 2015;26(10):783–93. https://doi.org/10.1080/1062936X.2015.1078407Lagunin AA, Rudik AV, Pogodin PV, Savosina PI, Tarasova OA, Dmitriev AV, et al. CLC-Pred 2.0: a freely available web application for in silico prediction of human cell line cytotoxicity and molecular mechanisms of action for druglike compounds. Int J Mol Sci. 2023;24(2):1689. https://doi.org/10.3390/ijms24021689Lagunin AA, Rudik AV, Pogodin PV, Savosina PI, Tarasova OA, Dmitriev AV, et al. CLC-Pred 2.0: a freely available web application for in silico prediction of human cell line cytotoxicity and molecular mechanisms of action for druglike compounds. Int J Mol Sci. 2023;24(2):1689. https://doi.org/10.3390/ijms24021689Lagunin A, Ivanov S, Rudik A, Filimonov D, Poroikov V. DIGEP-Pred: web-service for in silico prediction of drug-induced expression profiles based on structural formula. Bioinformatics. 2013;29(16):2062–63. https://doi.org/10.1093/bioinformatics/btt322Lagunin A, Ivanov S, Rudik A, Filimonov D, Poroikov V. DIGEP-Pred: web-service for in silico prediction of drug-induced expression profiles based on structural formula. Bioinformatics. 2013;29(16):2062–63. https://doi.org/10.1093/bioinformatics/btt322Ivanov SM, Lagunin AA, Rudik AV, Filimonov DA, Poroikov VV. ADVERPred — web service for prediction of adverse effects of drugs. J Chem Inf Model. 2018;58(1):8–11. https://doi.org/10.1021/acs.jcim.7b00568Ivanov SM, Lagunin AA, Rudik AV, Filimonov DA, Poroikov VV. ADVERPred — web service for prediction of adverse effects of drugs. J Chem Inf Model. 2018;58(1):8–11. https://doi.org/10.1021/acs.jcim.7b00568Lagunin A, Rudik A, Filimonov D, Druzhilovskiy D, Poroikov V. ROSC-Pred: web-service for rodent organ-specific carcinogenicity prediction. Bioinformatics. 2018;34(4):710–12. https://doi.org/10.1093/bioinformatics/btx678Lagunin A, Rudik A, Filimonov D, Druzhilovskiy D, Poroikov V. ROSC-Pred: web-service for rodent organ-specific carcinogenicity prediction. Bioinformatics. 2018;34(4):710–12. https://doi.org/10.1093/bioinformatics/btx678Lagunin A, Zakharov A, Filimonov D, Poroikov V. QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol Inform. 2011;30 (2–3):241–50. https://doi.org/10.1002/minf.201000151Lagunin A, Zakharov A, Filimonov D, Poroikov V. QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol Inform. 2011;30 (2–3):241–50. https://doi.org/10.1002/minf.201000151Zakharov AV, Lagunin AA, Filimonov DA, Poroikov VV. Quantitative prediction of antitarget interaction profiles for chemical compounds. Chem Res Toxicol. 2012;25(11):2378–85. https://doi.org/10.1021/tx300247rZakharov AV, Lagunin AA, Filimonov DA, Poroikov VV. Quantitative prediction of antitarget interaction profiles for chemical compounds. Chem Res Toxicol. 2012;25(11):2378–85. https://doi.org/10.1021/tx300247rDmitriev AV, Filimonov DA, Rudik AV, Pogodin PV, Karasev DA, Lagunin AA, Poroikov VV. Drug-drug interaction prediction using PASS. SAR QSAR Environ Res. 2019;30(9):655–64. https://doi.org/10.1080/1062936X.2019.1653966Dmitriev AV, Filimonov DA, Rudik AV, Pogodin PV, Karasev DA, Lagunin AA, Poroikov VV. Drug-drug interaction prediction using PASS. SAR QSAR Environ Res. 2019;30(9):655–64. https://doi.org/10.1080/1062936X.2019.1653966Короткевич ЕИ, Рудик АВ, Дмитриев АВ, Лагунин АА, Филимонов ДА. Прогноз метаболической стабильности ксенобиотиков программами PASS и GUSAR. Биомедицинская химия. 2021;67(3):295–9. https://doi.org/10.18097/PBMC20216703295Korotkevich EI, Rudik AV, Dmitriev AV, Lagunin AA, Filimonov DA. Predict of metabolic stability of xenobiotics by the PASS and GUSAR programs. Biomeditsinskaya Khimiya. 2021;67(3):295–9 (In Russ.). https://doi.org/10.18097/PBMC20216703295Rudik A, Dmitriev A, Lagunin A, Filimonov D, Poroikov V. SOMP: web-service for in silico prediction of sites of metabolism for drug-like compounds. Bioinformatics. 2015;31(12):2046–8. https://doi.org/10.1093/bioinformatics/btv087Rudik A, Dmitriev A, Lagunin A, Filimonov D, Poroikov V. SOMP: web-service for in silico prediction of sites of metabolism for drug-like compounds. Bioinformatics. 2015;31(12):2046–8. https://doi.org/10.1093/bioinformatics/btv087Rudik AV, Dmitriev AV, Lagunin AA, Filimonov DA, Poroikov VV. Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics. J Cheminform. 2016;8:68. https://doi.org/10.1186/s13321-016-0183-xRudik AV, Dmitriev AV, Lagunin AA, Filimonov DA, Poroikov VV. Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics. J Cheminform. 2016;8:68. https://doi.org/10.1186/s13321-016-0183-xRudik AV, Dmitriev AV, Lagunin AA, Filimonov DA, Poroikov VV. Metabolism sites prediction based on xenobiotics structural formulae and PASS prediction algorithm. J Chem Inf Model. 2014;54(2):498–507. https://doi.org/10.1021/ci400472jRudik AV, Dmitriev AV, Lagunin AA, Filimonov DA, Poroikov VV. Metabolism sites prediction based on xenobiotics structural formulae and PASS prediction algorithm. J Chem Inf Model. 2014;54(2):498–507. https://doi.org/10.1021/ci400472jRudik AV, Bezhentsev VM, Dmitriev AV, Druzhilovskiy DS, Lagunin AA, Filimonov DA, Poroikov VV. MetaTox: web application for predicting structure and toxicity of xenobiotics’ metabolites. J Chem Inf Model. 2017;57(4):638–42. https://doi.org/10.1021/acs.jcim.6b00662Rudik AV, Bezhentsev VM, Dmitriev AV, Druzhilovskiy DS, Lagunin AA, Filimonov DA, Poroikov VV. MetaTox: web application for predicting structure and toxicity of xenobiotics’ metabolites. J Chem Inf Model. 2017;57(4):638–42. https://doi.org/10.1021/acs.jcim.6b00662Rudik A, Bezhentsev V, Dmitriev A, Lagunin A, Filimonov D, Poroikov V. MetaTox — web application for generation of metabolic pathways and toxicity estimation. J Bioinform Comput Biol. 2019;17(1):1940001. https://doi.org/10.1142/S0219720019400018Rudik A, Bezhentsev V, Dmitriev A, Lagunin A, Filimonov D, Poroikov V. MetaTox — web application for generation of metabolic pathways and toxicity estimation. J Bioinform Comput Biol. 2019;17(1):1940001. https://doi.org/10.1142/S0219720019400018Rudik A, Dmitriev A, Lagunin A, Filimonov D, Poroikov V. MetaPASS: a web application for analyzing the biological activity spectrum of organic compounds taking into account their biotransformation. Mol Inform. 2021;40(4):2000231. https://doi.org/10.1002/minf.202000231Rudik A, Dmitriev A, Lagunin A, Filimonov D, Poroikov V. MetaPASS: a web application for analyzing the biological activity spectrum of organic compounds taking into account their biotransformation. Mol Inform. 2021;40(4):2000231. https://doi.org/10.1002/minf.202000231Раевский ОА, Солодова СЛ, Лагунин АА, Поройков ВВ. Компьютерное моделирование проницаемости физиологически активных веществ через гематоэнцефалический барьер. Биомедицинская химия. 2014;60(2):161–81. https://doi.org/10.18097/PBMC20146002161Raevsky OA, Solodova SL, Lagunin AA, Poroikov VV. Computer modeling of blood–brain barrier permeability of physiologically active compounds. Biomeditsinskaya Khimiya. 2014;60(2):161–81 (In Russ.). https://doi.org/10.18097/PBMC20146002161Ivanov SM, Lagunin AA, Poroikov VV. In silico assessment of adverse drug reactions and associated mechanisms. Drug Discov Today. 2016;21(1):58–71. https://doi.org/10.1016/j.drudis.2015.07.018Ivanov SM, Lagunin AA, Poroikov VV. In silico assessment of adverse drug reactions and associated mechanisms. Drug Discov Today. 2016;21(1):58–71. https://doi.org/10.1016/j.drudis.2015.07.018Ivanov SM, Lagunin AA, Pogodin PV, Filimonov DA, Poroikov VV. Identification of drug targets related to the induction of ventricular tachyarrhythmia through systems chemical biology approach. Toxicol Sci. 2015;145(2):321–36. https://doi.org/10.1093/toxsci/kfv054Ivanov SM, Lagunin AA, Pogodin PV, Filimonov DA, Poroikov VV. Identification of drug targets related to the induction of ventricular tachyarrhythmia through systems chemical biology approach. Toxicol Sci. 2015;145(2):321–36. https://doi.org/10.1093/toxsci/kfv054Koborova ON, Filimonov DA, Zakharov AV, Lagunin AA, Ivanov SM, Kel A, Poroikov VV. In silico method for identification of promising anticancer drug targets. SAR QSAR Environ Res. 2009;20 (7–8):755–66. https://doi.org/10.1080/10629360903438628Koborova ON, Filimonov DA, Zakharov AV, Lagunin AA, Ivanov SM, Kel A, Poroikov VV. In silico method for identification of promising anticancer drug targets. SAR QSAR Environ Res. 2009;20 (7–8):755–66. https://doi.org/10.1080/10629360903438628Ivanov SM, Lagunin AA, Pogodin PV, Filimonov DA, Poroikov VV. Identification of drug-induced myocardial infarction-related protein targets through the prediction of drug–target interactions and analysis of biological processes. Chem Res Toxicol. 2014;27(7):1263–81. https://doi.org/10.1021/tx500147dIvanov SM, Lagunin AA, Pogodin PV, Filimonov DA, Poroikov VV. Identification of drug-induced myocardial infarction-related protein targets through the prediction of drug–target interactions and analysis of biological processes. Chem Res Toxicol. 2014;27(7):1263–81. https://doi.org/10.1021/tx500147dПоройков ВВ, Филимонов ДА, Глориозова ТА, Лагунин АА, Дружиловский ДС, Рудик АВ и др. Компьютерный прогноз спектров биологической активности органических соединений: возможности и ограничения. Известия Академии наук. Серия химическая. 2019;(12):2143–54. EDN: YQLMTT https://doi.org/10.1007/s11172-019-2683-0Poroikov VV, Filimonov DA, Gloriozova TA, Lagunin AA, Druzhilovskiy DS, Rudik AV, et al. Computer-aided prediction of biological activity spectra for organic compounds: the possibilities and limitations. Russ Chem Bull. 2019;(12):2143–54. https://doi.org/10.1007/s11172-019-2683-0Ivanov S, Lagunin A, Filimonov D, Poroikov V. Assessment of the cardiovascular adverse effects of drug–drug interactions through a combined analysis of spontaneous reports and predicted drug–target interactions. PLoS Comput Biol. 2019;15(7):e1006851. https://doi.org/10.1371/journal.pcbi.1006851Ivanov S, Lagunin A, Filimonov D, Poroikov V. Assessment of the cardiovascular adverse effects of drug–drug interactions through a combined analysis of spontaneous reports and predicted drug–target interactions. PLoS Comput Biol. 2019;15(7):e1006851. https://doi.org/10.1371/journal.pcbi.1006851Ivanov S, Lagunin A, Filimonov D, Poroikov V. Relationships between the structure and severe drug-induced liver injury for low, medium and high doses of drugs. Chem Res Toxicol. 2022;35(3):402–11. https://doi.org/0.1021/acs.chemrestox.1c00307Ivanov S, Lagunin A, Filimonov D, Poroikov V. Relationships between the structure and severe drug-induced liver injury for low, medium and high doses of drugs. Chem Res Toxicol. 2022;35(3):402–11. https://doi.org/0.1021/acs.chemrestox.1c00307Сухачёв ВС, Иванов СМ, Дмитриев АВ, Прогнозирование неблагоприятных эффектов межлекарственных взаимодействий на сердечно-сосудистую систему на основе анализа связей «структура–активность». Биохимия. 2023;88(5):773–84. https://doi.org/10.31857/S0320972523050068Sukhachev VS, Ivanov SM, Dmitriev AV. Prediction of adverse effects of drug–drug interactions on cardiovascular system based on the analysis of structure–activity relationships. Biochemistry (Moscow). 2023;88(5):630-39. https://doi.org/10.1134/S0006297923050061Irurzun-Arana I, Rackauckas C, McDonald TO, Trocóniz IF. Beyond deterministic models in drug discovery and development. Trends Pharmacol Sci. 2020;41(11):882–95. https://doi.org/10.1016/j.tips.2020.09.005Irurzun-Arana I, Rackauckas C, McDonald TO, Trocóniz IF. Beyond deterministic models in drug discovery and development. Trends Pharmacol Sci. 2020;41(11):882–95. https://doi.org/10.1016/j.tips.2020.09.005Blanco MJ, Gardinier KM, Namchuk MN. Advancing new chemical modalities into clinical studies. ACS Med Chem Lett. 2022;13(11):1691–8. https://doi.org/10.1021/acsmedchemlett.2c00375Blanco MJ, Gardinier KM, Namchuk MN. Advancing new chemical modalities into clinical studies. ACS Med Chem Lett. 2022;13(11):1691–8. https://doi.org/10.1021/acsmedchemlett.2c00375Bonner S, Barrett IP, Ye C, Swiers R, Engkvist O, Bender A, Hoyt CT, Hamilton WL. A review of bio medical datasets relating to drug discovery: a knowledge graph perspective. Brief Bioinform. 2022;23(6):bbac404. https://doi.org/10.1093/bib/bbac404Bonner S, Barrett IP, Ye C, Swiers R, Engkvist O, Bender A, Hoyt CT, Hamilton WL. A review of bio medical datasets relating to drug discovery: a knowledge graph perspective. Brief Bioinform. 2022;23(6):bbac404. https://doi.org/10.1093/bib/bbac404Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11): 682–90. https://doi.org/10.1038/nchembio.118Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11): 682–90. https://doi.org/10.1038/nchembio.118Loscalzo J, Kohane I, Barabasi AL. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol Syst Biol. 2007;3:124. https://doi.org/10.1038/msb4100163Loscalzo J, Kohane I, Barabasi AL. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol Syst Biol. 2007;3:124. https://doi.org/10.1038/msb4100163Tarasova OA, Urusova AF, Filimonov DA, Nicklaus MC, Zakharov AV, Poroikov VV. QSAR modeling using large-scale databases: case study for HIV-1 reverse transcriptase inhibitors. J Chem Inf Model. 2015;55(7):1388–99. https://doi.org/10.1021/acs.jcim.5b00019Tarasova OA, Urusova AF, Filimonov DA, Nicklaus MC, Zakharov AV, Poroikov VV. QSAR modeling using large-scale databases: case study for HIV-1 reverse transcriptase inhibitors. J Chem Inf Model. 2015;55(7):1388–99. https://doi.org/10.1021/acs.jcim.5b00019Alharbi E, Gadiya Y, Henderson D, Zaliani A, Delfin-Rossaro A, Cambon-Thomsen A, et al. Selection of data sets for FAIRification in drug discovery and development: which, why, and how? Drug Discov Today. 2022;27(8):2080–5. https://doi.org/10.1016/j.drudis.2022.05.010Alharbi E, Gadiya Y, Henderson D, Zaliani A, Delfi nRossaro A, Cambon-Thomsen A, et al. Selection of data sets for FAIRification in drug discovery and development: which, why, and how? Drug Discov Today. 2022;27(8):2080–5. https://doi.org/10.1016/j.drudis.2022.05.010Перфилова ВН. Возможности и перспективы доклинической оценки лекарственной безопасности с использованием альтернативных методов: опыт реализации программы «Токсикология в XXI веке» в США. Безопасность и риск фармакотерапии. 2023. https://doi.org/10.30895/2312-7821-2023-379Perfilova VN. Opportunities and prospects for preclinical drug safety assessment using alternative methods: experience from the Toxicology in the 21st Century (Tox21) programme in the USA. Safety and Risk of Pharmacotherapy. 2023 (In Russ). https://doi.org/10.30895/2312-7821-2023-379Tarasova OA, Biziukova NYu, Filimonov DA, Poroikov VV, Nicklaus MC. Data mining approach for extraction of useful information about biologically active compounds from publications. Journal of Chemical Information and Modeling. 2019;59(9):3635–44. https://doi.org/10.1021/acs.jcim.9b00164Tarasova OA, Biziukova NYu, Filimonov DA, Poroikov VV, Nicklaus MC. Data mining approach for extraction of useful information about biologically active compounds from publications. Journal of Chemical Information and Modeling. 2019;59(9):3635–44. https://doi.org/10.1021/acs.jcim.9b00164Tarasova OA, Biziukova NYu, Rudik AV, Dmitriev AV, Filimonov DA, Poroikov VV. Extraction of data on parent compounds and their metabolites from texts of scientific abstracts. J Chem Inf Model. 2021;61(4):1683–90. https://doi.org/10.1021/acs.jcim.0c01054Tarasova OA, Biziukova NYu, Rudik AV, Dmitriev AV, Filimonov DA, Poroikov VV. Extraction of data on parent compounds and their metabolites from texts of scientific abstracts. J Chem Inf Model. 2021;61(4):1683–90. https://doi.org/10.1021/acs.jcim.0c01054Tarasova OA, Rudik AV, Biziukova NYu, Filimonov DA, Poroikov VV. Cheminform. 2022;14:55. https://doi.org/10.1186/s13321-022-00633-4Tarasova OA, Rudik AV, Biziukova NYu, Filimonov DA, Poroikov VV. Cheminform. 2022;14:55. https://doi.org/10.1186/s13321-022-00633-4

The authors declare that there are no conflicts of interest present.