УДК: 004.8:615.012
DOI: https://doi.org/10.52540/2074-9457.2026.1.72
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А. С. Максимов¹, Ж. М. Дергачёва²

ПРИМЕНЕНИЕ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ В РАННЕЙ РАЗРАБОТКЕ ЛЕКАРСТВЕННЫХ СРЕДСТВ

¹ООО «Искамед», г. Минск, Республика Беларусь

²Витебский государственный ордена Дружбы народов медицинский университет,  г. Витебск, Республика Беларусь

В статье представлен систематический обзор применения методов машинного обучения на раннем этапе разработки лекарственных средств. Рассмотрены три ключевые стадии: поиск соединений-хитов, установление механизма действия и трансляционные исследования. Показано, что графовые нейронные сети (D-MPNN) и гибридные подходы (Deep Docking) позволяют значительно повысить эффективность виртуального скрининга, а генеративные модели – вариационные автоэнкодеры, нормализующие потоки, диффузионные модели и трансформеры – обеспечивают целенаправленный дизайн новых молекул с заданными свойствами, выходя за пределы существующих химических библиотек. В области предсказания структуры белков проанализирована эволюция от AlphaFold2 и RoseTTAFold к языковым моделям белка (OmegaFold, ESMFold), которые позволяют преодолевать ограничения, связанные с отсутствием эволюционной информации. Рассмотрены диффузионные подходы к молекулярному докингу (DiffDock) и дизайну белков (RFdiffusion), демонстрирующие переход от эвристических методов к непрерывному генеративному моделированию. Отдельное внимание уделено трансферному обучению на примере Geneformer для анализа одноклеточных транскриптомов, а также интеграции прогностических моделей ADMET (ADMET-AI) для многопараметрической оптимизации кандидатов. Сформулированы ключевые вызовы: интерпретируемость моделей, экспериментальная валидация in silico предсказаний, регуляторное признание вычислительных подходов и необходимость развития открытых репозиториев данных высокого качества. Сделан вывод, что методы машинного обучения не заменяют, а дополняют традиционные экспериментальные подходы, формируя основу для нового междисциплинарного направления на стыке химической биологии и data science.

Ключевые слова: машинное обучение, ранняя разработка лекарственных средств, виртуальный скрининг, генеративные модели, графовые нейронные сети, предсказание структуры белков, AlphaFold, языковые модели белка, молекулярный докинг, диффузионные модели, ADMET-прогнозирование, токсичность, трансферное обучение, интерпретируемость моделей.

SUMMARY

A. S. Maksimov, Zh. M. Dergacheva

APPLICATION OF MACHINE LEARNING METHODS IN EARLY DRUG DEVELOPMENT

This article presents a systematic review of machine learning (ML) methods application at an early drug development. Three key phases are considered: top-connections search, mechanism of action elucidation, and translational research. Graph neural networks (D-MPNN) and hybrid approaches (Deep Docking) are shown to substantially enhance virtual screening efficiency, while generative models–variational autoenco-ders normalizing flows, diffusion models and transformers–enable targeted de novo design of molecules with the defined pro-perties leaving beyond existing chemical libraries. In the field of protein structure prediction, the evolution from AlphaFold2 and RoseTTAFold to protein language models (OmegaFold, ESMFold) highlighting their ability to overcome limitations associated with the lack of evolutionary information is analyzed. Diffusion-based approaches to molecular docking (DiffDock) and protein design (RFdiffusion) demonstrating the transition from heuristic methods to continuous generative modeling are discussed. Special attention is given to transfer learning exemplified by Geneformer for single-cell transcriptome analysis, as well as to the integration of ADMET predictive models (ADMET-AI) for multi-parameter optimization of drug candidates. Key challenges are identified: model interpretability, expe-rimental validation of in silico predictions, regulatory acceptance of computational approaches, and the need for high-quality open data repositories. It is concluded that ML methods complement rather than replace traditional experimental approaches, forming the basis for a new interdiscipli-nary field at the intersection of chemical biology and data science.

Keywords: machine learning, early drug development, virtual screening, generative models, graph neural networks, protein structure prediction, AlphaFold, protein language models, molecular docking, diffusion models, ADMET prediction, toxicity, transfer learning, model interpretability.

ЛИТЕРАТУРА

1. Wouters, O. J. Estimated research and development investment needed to bring a new medicine to market, 2009–2018 / O. J. Wouters, M. McKee, J. Luyten // JAMA. – 2020. – Vol. 323, N 9. – P. 844–853. – DOI: 10.1001/jama.2020.1166.

2. Target identification and mechanism of action in chemical biology and drug discovery / M. Schenone, V. Dančik, B. K. Wagner, P. A. Clemons // Nature chemical biology. – 2013. – Vol. 9, N 4. – P. 232–240. – DOI: 10.1038/nchembio.1199.

3. Ashenden, S. K. The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry / S. K. Ashenden. – Cambridge : Academic Press, 2021. – 264 p. – Chap. 6.

4. Smietana, K. Trends in clinical success rates / K. Smietana, M. Siatkowski, M. Møller // Nature reviews. Drug discovery. – 2016. – Vol. 15, N 6. – P. 379–380. – DOI: 10.1038/nrd.2016.85.

5. Harrison, R. K. Phase II and phase III failures: 2013–2015 / R. K. Harrison // Nature reviews. Drug discovery. – 2016. – Vol. 15, N 12. – P. 817–818. – DOI: 10.1038/nrd.2016.184.

6. Dowden, H. Trends in clinical success rates and therapeutic focus / H. Dowden, J. Munro // Nature reviews. Drug discovery. – 2019. – Vol. 18, N 7. – P. 495–496. – DOI: 10.1038/d41573-019-00074-z.

7. Computer vision for autonomous vehicles: problems, datasets and state of the art / J. Janai, F. Güney, A. Behl, A. Geiger // Foundations and Trends in Computer Graphics and Vision. – 2020. – Vol. 12, N 1–3. – P. 1–308. – DOI: 10.1561/0600000079.

8. Machine learning and natural language processing in psychotherapy research: alliance as example use case / S. B. Goldberg, N. Flemotomos, V. R. Martinez [et al.] // Journal of counseling psychology. – 2020. – Vol. 67, N 4. – P. 438–448. – DOI: 10.1037/cou0000382.

9. Peterson, A. A. Small-molecule discovery through DNA-encoded libraries / A. A. Peterson, D. R. Liu // Nature reviews. Drug discovery. – 2023. – Vol. 22, N 9. – P. 699–722. – DOI: 10.1038/s41573-023-00713-6.

10. Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss function / K. S. Lim, A. G. Reidenbach, B. K. Hua [et al.] // Journal of chemical informaition and modeling. – 2022. – Vol. 62, N 10. – P. 2316–2331. – DOI: 10.1021/acs.jcim.2c00041.

11. Machine-learning-based data analysis method for cell-based selection of DNA-encoded libraries / R. Hou, C. Xie, Y. Gui [et al.] // ACS omega. – 2023. – Vol. 8, N 21. – P. 19057–19071. – DOI: 10.1021/acsomega.3c02152.

12. Applications of single-cell RNA sequencing in drug discovery and development / B. Van de Sande, J. S. Lee, E. Mutasa-Gottgens [et al.] // Nature reviews. Drug discovery. – 2023. – Vol. 22, N 6. – P. 496–520. – DOI: 10.1038/s41573-023-00688-4.

13. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data / F. Yang, W. Wang, F. Wang [et al.] // Nature machine intelligence. – 2022. – Vol. 4, N 10. – P. 852–866. – DOI: 10.1038/s42256-022-00534-z.

14. Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data / J. Chen, X. Wang, A. Ma [et al.] // Nature communications. – 2022. – Vol. 13, N 1. – Art. 6494. – DOI: 10.1038/s41467-022-34277-7.

15. A multi-scale convolutional neural network for phenotyping high-content cellular images / W. J. Godinez, I. Hossain, S. E. Lazic [et al.] // Bioinformatics. – 2017. – Vol. 33, N 13. – P. 2010–2019. – DOI: 10.1093/bioinformatics/btx069.

16. A deep learning approach to antibiotic discovery / J. M. Stokes, K. Yang, K. Swanson [et al.] // Cell. – 2020. – Vol. 180, N 4. – P. 688–702. – DOI: 10.1016/j.cell.2020.01.021.

17. Convolutional embedding of attributed molecular graphs for physical property prediction / C. W. Coley, R. Barzilay, W. H. Green [et al.] // Journal of chemical information and modeling. – 2017. – Vol. 57, N 8. – P. 1757–1772. – DOI: 10.1021/acs.jcim.6b00601.

18. Deep learning identifies synergistic drug combinations for treating COVID-19 / W. Jin, J. M. Stokes, R. T. Eastman [et al.] // Proceedings of the National Academy of Sciences of the United States of America. – 2021. – Vol. 118, N 39. – Art. e2105070118. – DOI: 10.1073/pnas.2105070118.

19. Applications of machine learning in drug discovery and development / J. Vamathevan, D. Clark, P. Czodrowski [et al.] // Nature reviews. Drug discovery. – 2019. – Vol. 18, N 6. – P. 463–477. – DOI: 10.1038/s41573-019-0024-5.

20. Rogers, D. Extended-connectivity fingerprints / D. Rogers, M. Hahn // Journal of chemical information and modeling. – 2010. – Vol. 50, N 5. – P. 742–754. – DOI: 10.1021/ci100050t.

21. Database fingerprint (DFP): an approach to represent molecular databases / E. Fernández-De Gortari, C. R. García-Jacas, K. Martinez-Mayorga, J. L. Medina-Franco // Journal of cheminformatics. – 2017. – Vol. 9, N 1. – Art. 9. – Doi: 10.1186/s13321-017-0195-1.

22. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii / G. Liu, D. B. Catacutan, K. Rathod [et al.] // Nature chemical biology. – 2023. – Vol. 19, N 11. – P. 1342–1350. – DOI: 10.1038/s41589-023-01349-8.

23. Analyzing learned molecular representations for property prediction / K. Yang, K. Swanson, W. Jin [et al.] // Journal of chemical information and modeling. – 2019. – Vol. 59, N 8. – P. 3370–3388. – DOI: 10.1021/acs.jcim.9b00237.

24. The Drug Repurposing Hub: a next-generation drug library and information resource / S. M. Corsello, J. A. Bittker, Z. Liu [et al.] // Nature medicine. – 2017. – Vol. 23, N 4. – P. 405–408. – DOI: 10.1038/nm.4306.

25. Discovery of a structural class of antibiotics with explainable deep learning / F. Wong, E. J. Zheng, J. A. Valeri [et al.] // Nature. – 2024. – Vol. 626, N 7997. – P. 177–185. – DOI: 10.1038/s41586-023-06887-8.

26. A practical guide to large-scale docking / B. J. Bender, S. Gahbauer, A. Luttens [et al.] // Nature protocols. – 2021. – Vol. 16, N 10. – P. 4799–4832. – DOI: 10.1038/s41596-021-00597-z.

27. Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking / F. Gentile, J. C. Yaacoub, J. Gleave [et al.] // Nature protocols. – 2022. – Vol. 17, N 3. – P. 672–697. – DOI: 10.1038/s41596-021-00659-2.

28. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR / A. Tropsha, O. Isayev, A. Varnek [et al.] // Nature reviews. Drug discovery. – 2024. – Vol. 23, N 2. – P. 141–155. – DOI: 10.1038/s41573-023-00832-0.

29. Supercomputer-based ensemble docking drug discovery pipeline with application to Covid-19 / A.  Acharya, R. Agarwal, M. B. Baker [et al.] // Journal of chemical information and modeling. – 2020. – Vol. 60, N 12. – P. 5832–5852. – DOI: 10.1021/acs.jcim.0c01010.

30. A critical overview of computational approaches employed for COVID-19 drug discovery / E. N. Muratov, R. Amaro, C. H. Andrade [et al.] // Chemical Society reviews. – 2021. – Vol. 50, N 16. – P. 9121–9151. – DOI: 10.1039/d0cs01065k.

31. Sterling, T. ZINC 15 – ligand discovery for everyone / T. Sterling, J. J. Irwin // Journal of chemical information and modeling. – 2015. – Vol. 55, N 11. – P. 2324–2337. – DOI: 10.1021/acs.jcim.5b00559.

32. Non-covalent SARS-CoV-2 Mpro inhibitors developed from in silico screen hits / G. G. Rossetti, M. A. Ossorio, S. Rempel [et al.] // Scientific reports. – 2022. – Vol. 12, N 1. – Art. 2505. – DOI: 10.1038/s41598-022-06306-4.

33. Reymond, J. L. The chemical space project / J. L. Reymond // Accounts of chemical research. – 2015. – Vol. 48, N 3. – P. 722–730. – DOI: 10.1021/ar500432k.

34. Automatic chemical design using a data-driven continuous representation of molecules / R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud [et al.] // ACS central science. – 2018. – Vol. 4, N 2. – P. 268–276. – DOI: 10.1021/acscentsci.7b00572.

35. Anstine, D. M. Generative models as an emerging paradigm in the chemical sciences / D. M. Anstine, O. Isayev // Journal of the American Chemical Society. – 2023. – Vol. 145, N 16. – P. 8736–8750. – DOI: 10.1021/jacs.2c13467.

36. Jin, W. Junction tree variational autoencoder for molecular graph generation / W. Jin, R. Barzilay, T. Jaakkola // arXiv. – 2018. – 18 Feb. – URL: https://arxiv.org/abs/1802.04364 (date of access: 16.03.2026).

37. Design of potent antimalarials with generative chemistry / W. J. Godinez, E. J. Ma, A. T. Chao [et al.] // Nature machine intelligence. – 2022. – Vol. 4, N 2. – P. 180–186. – DOI: 10.1038/s42256-022-00448-w.

38. Walters, W. P. Assessing the impact of generative AI on medicinal chemistry / W. P. Walters, M. Murcko // Nature biotechnology. – 2020. – Vol. 38, N 2. – P. 143–145. – DOI: 10.1038/s41587-020-0418-2.

39. Deep learning tools to accelerate antibiotic discovery / A. Cesaro, M. Bagheri, M. Torres [et al.] // Expert opinion on drug discovery. – 2023. – Vol. 18, N 11. – P. 1245–1257. – DOI: 10.1080/17460441.2023.2250721.

40. Rezende, D. J. Variational inference with normalizing flows / D. J. Rezende, S. Mohamed // International Conference on Machine Learning : proceedings of the 32nd International Conference on Machine Learning / ed.: F. Bach, D. Blei. – 2015. – Vol. 37. – P. 1530–1538.

41. Shekhovtsov, A. VAE approximation error: ELBO and exponential families / A. Shekhovtsov, D. Schlesinger, B. Flach // arXiv. – 2021. – 18 Feb. – URL: https://arxiv.org/abs/2102.09310 (date of access: 16.03.2026).

42. GraphAF: a flow-based autoregressive model for molecular graph generation / C. Shi, M. Xu, Z. Zhu [et al.] // arXiv. – 2020. – 27 Jan. – URL: https://arxiv.org/abs/2001.09382 (date of access: 16.03.2026).

43. Equivariant diffusion for molecule generation in 3D / E. Hoogeboom, V. G. Satorras, C. Vignac, M. Welling // International Conference on Machine Learning : proceedings of the 39th International Conference on Machine Learning. – 2022. – Vol. 162. – P. 8867–8887.

44. Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules / D. Weininger // Journal of Chemical Information and Computer Sciences. – 1988. – Vol. 28, N 1. – P. 31–36. – DOI: 10.1021/ci00057a005.

45. Grisoni, F. Chemical language models for de novo drug design: challenges and opportunities / F. Grisoni // Current opinion in structural biology. – 2023. – Vol. 79. – Art. 102527. – DOI: 10.1016/j.sbi.2023.102527.

46. Flam-Shepherd, D. Language models can learn complex molecular distributions / D. Flam-Shepherd, K. Zhu, A. Aspuru-Guzik // Nature communications. – 2022. – Vol. 13, N 1. – Art. 3293. – DOI: 10.1038/s41467-022-30839-x.

47. Chemical language models enable navigation in sparsely populated chemical space / M. A. Skinnider, R. Greg Stacey, D. S. Wishart, L. J. Foster // Nature machine intelligence. – 2021. – Vol. 3, N 9. – P. 759–770. – DOI: 10.1038/s42256-021-00368-1.

48. Generative molecular design in low data regimes / M. Moret, L. Friedrich, F. Grisoni [et al.] // Nature machine intelligence. – 2020. – Vol. 2, N 3 – P. 171–180. – DOI: 10.1038/s42256-020-0160-y.

49. De novo design of Nurr1 agonists via fragment-augmented generative deep learning in low-data regime / M. Ballarotto, S. Willems, T. Stiller [et al.] // Journal of medicinal chemistry. – 2023. – Vol. 66, N 12. – P. 8170–8177. – DOI: 10.1021/acs.jmedchem.3c00485.

50. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design / M. Moret, I. P. Angona, L. Cotos [et al.] // Nature communications. – 2023. – Vol. 14, N 1. – Art. 114. – DOI: 10.1038/s41467-022-35692-6.

51. Combining generative artificial intelligence and on-chip synthesis for de novo drug design / F. Grisoni, B. J. H. Huisman, A. L. Button [et al.] // Science advances. – 2021. – Vol. 7, N 24. – Art. eabg3338. – DOI: 10.1126/sciadv.abg3338.

52. De novo design of bioactive small molecules by artificial intelligence / D. Merk, L. Friedrich, F. Grisoni, G. Schneider // Molecular informatics. – 2018. – Vol. 37, N 1/2. – Art. 1700153. – DOI: 10.1002/minf.201700153.

53. Attention is all you need / A. Vaswani, N. Shazeer, N. Parmar [et al.] // arXiv. – 2017. – 12 June. – URL: https://arxiv.org/abs/1706.03762 (date of access: 16.03.2026).

54. MolGPT: molecular generation using a transformer-decoder model / V. Bagal, R. Aggarwal, P. K. Vinod, U. D. Priyakumar // Journal of chemical information and modeling. – 2021. – Vol. 62, N 9. – P. 2064–2076. – DOI: 10.1021/acs.jcim.1c00600.

55. GuacaMol: benchmarking models for de novo molecular design / N. Brown, M. Fiscato, M. H. S. Segler, A. C. Vaucher // Journal of chemical information and modeling. – 2019. – Vol. 59, N 3. – P. 1096–1108. – DOI: 10.1021/acs.jcim.8b00839.

56. Molecular sets (MOSES): a benchmarking platform for molecular generation models / D. Polykovskiy, A. Zhebrak, B. Sanchez-Lengeling [et al.] // Frontiers in pharmacology. – 2020. – Vol. 11. – Art. 565644. – DOI: 10.3389/fphar.2020.565644.

57. Autonomous chemical research with large language models / D. A. Boiko, R. MacKnight, B. Kline, G. Gomes // Nature. – 2023. – Vol. 624. – P. 570–578. – DOI: 10.1038/s41586-023-06792-0.

58. Leveraging large language models for predictive chemistry / K. M. Jablonka, P. Schwaller, A. Ortega-Guerrero, B. Smit // Nature machine intelligence. – 2024. – Vol. 6, N 2. – P. 161–169. – DOI: 10.1038/s42256-023-00788-1.

59. Born, J. Regression Transformer enables concurrent sequence regression and generation for molecular language modelling / J. Born, M. Manica // Nature machine intelligence. – 2023. – Vol. 5, N 4. – P. 432–444. – DOI: 10.1038/s42256-023-00639-z.

60. Neural scaling of deep chemical models / N. C. Frey, R. Soklaski, S. Axelrod [et al.] // Nature machine intelligence. – 2023. – Vol. 5, N 11. – P. 1297–1305. – DOI: 10.1038/s42256-023-00740-3.

61. Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem / D. Grechishnikova // Scientific reports. – 2021. – Vol. 11, N 1. – Art. 321. – DOI: 10.1038/s41598-020-79682-4.

62. Highly accurate protein structure prediction with AlphaFold / J. Jumper, R. Evans, A. Pritzel [et al.] // Nature. – 2021. – Vol. 596, N 7873. – P. 583–589. – DOI: 10.1038/s41586-021-03819-2.

63. Structure of the decoy module of human glycoprotein 2 and uromodulin and its interaction with bacterial adhesin FimH / A. Stsiapanava, C. Xu, S. Nishio [et al.] // Nature structural & molecular biology. – 2022. – Vol. 29, N 3. – P. 190–193. – DOI: 10.1038/s41594-022-00729-3.

64. Cryo-EM structures of human hepatitis B and woodchuck hepatitis virus small spherical subviral particles / H. Liu, X. Hong, J. Xi [et al.] // Science advances. – 2022. – Vol. 8, N 31. – Art. eabo4184. – DOI: 10.1126/sciadv.abo4184.

65. AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor / F. Ren, X. Ding, M. Zheng [et al.] // Chemical science. – 2023. – Vol. 14, N 6. – P. 1443–1452. – DOI: 10.48550/arXiv.2201.09647.

66. Structural comparison and drug screening of spike proteins of ten SARS-CoV-2 variants / Q. Yang, X. Lian, A. A. S. Syed [et al.] // Research (Washington, D.C.). – 2022. – Vol. 2022. – Art. 9781758. – DOI: 10.34133/2022/9781758.

67. Highly accurate protein structure prediction and drug screen of monkeypox virus proteome / Q. Yang, D. Xia, A. A. S. Syed [et al.] // The Journal of infection. – 2023. – Vol. 86, N 1. – P. 66–117. – DOI: 10.1016/j.jinf.2022.08.006.

68. Chemistry42: an AI-driven platform for molecular design and optimization / Y. A. Ivanenkov, D. Polykovskiy, D. Bezrukov [et al.] // Journal of Chemical Information and Modeling. – 2023. – Vol. 63, N 3. – P. 695–701. – DOI: 10.1021/acs.jcim.2c01191.

69. Accurate prediction of protein structures and interactions using a three-track neural network / M. Baek, F. DiMaio, I. Anishchenko [et al.] // Science. – 2021. – Vol. 373, N 6557. – P. 871–876. – DOI: 10.1126/science.abj8754.

70. The Protein Data Bank / H. M. Berman, J. Westbrook, Z. Feng [et al.] // Nucleic acids research. – 2000. – Vol. 28, N 1. – P. 235–242. – DOI: 10.1093/nar/28.1.235.

71. Van Wart, H. E. The cysteine switch: a principle of regulation of metalloproteinase activity with potential applicability to the entire matrix metalloproteinase gene family / H. E. Van Wart, H. Birkedal-Hansen // Proceedings of the National Academy of Sciences of the United States of America. – 1990. – Vol. 87, N 14. – P. 5578–5582. – DOI: 10.1073/pnas.87.14.5578.

72. Michaud, J. M. A language model beats AlphaFold2 on orphans / J. M. Michaud, A. Madani, J. S. Fraser // Nature biotechnology. – 2022. – Vol. 40, N 11. – P. 1576–1577. – DOI: 10.1038/s41587-022-01466-0.

73. High-resolution de novo structure prediction from primary sequence / R. Wu, F. Ding, R. Wang [et al.] // bioRxiv. – 2022. – 21 July. – DOI:10.1101/2022.07.21.500999.

74. A method for multiple-sequence-alignment-free protein structure prediction using a protein language model / X. Fang, F. Wang, L. Liu [et al.] // Nature machine intelligence. – 2023. – Vol. 5, N 10. – P. 1087–1096. – DOI: 10.1038/s42256-023-00721-6.

75. Large language models generate functional protein sequences across diverse families / A. Madani, B. Krause, E. R. Greene [et al.] // Nature biotechnology. – 2023. – Vol. 41, N 8. – P. 1099–1106. – DOI: 10.1038/s41587-022-01618-2.

76. Evolutionary-scale prediction of atomic-level protein structure with a language model / Z. Lin, H. Akin, R. Rao [et al.] // Science. – 2023. – Vol. 379, N 6637. – P. 1123–1130. – DOI: 10.1126/science.ade2574.

77. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches / B. E. Suzek, Y. Wang, H. Huang [et al.] // Bioinformatics. – 2015. – Vol. 31, N 6. – P. 926–932. – DOI: 10.1093/bioinformatics/btu739.

78. DiffDock: diffusion steps, twists, and turns for molecular docking / G. Corso, H. Stärk, B. Ling [et al.]. // arXiv. – 2022. – 4 Oct. – URL: https://arxiv.org/abs/2210.01776 (date of access: 16.03.2026).

79. De novo design of protein structure and function with RFdiffusion / J. L. Watson, D. Juergens, N. R. Bennett [et al.] // Nature. – 2023. – Vol. 620, N 7976. – P. 1089–1100. – DOI: 10.1038/s41586-023-06415-8.

80. Transfer learning enables predictions in network biology / C. V. Theodoris, L. Xiao, A. Chopra [et al.] // Nature. – 2023. – Vol. 618, N 7965. – P. 616–624. – DOI: 10.1038/s41586-023-06139-9.

81. Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy / M. Chaffin, I. Papangeli, B. Simonson [et al.] // Nature. – 2022. – Vol. 608, N 7921. – P. 174–180. – DOI: 10.1038/s41586-022-04817-8.

82. Principles of early drug discovery / J. P. Hughes, S. S. Rees, S. B. Kalindjian, K. L. Philpott // British journal of pharmacology. – 2011. – Vol. 162, N 6. – P. 1239–1249. – DOI: 10.1111/j.1476-5381.2010.01127.x.

83. Goodnow, R. A. Hit and lead identification: integrated technology-based approaches / R. A. Goodnow // Drug discovery today. Technologies. – 2006. – Vol. 3, N 4. – P. 367–375. – DOI: 10.1016/j.ddtec.2006.12.009.

84. Transformer-based deep learning method for optimizing ADMET properties of lead compounds / L. Yang, C. Jin, G. Yang [et al.] // Physical chemistry chemical physics. – 2023. – Vol. 25, N 3. – P. 2377–2385. – DOI: 10.1039/d2cp05332b.

85. In silico prediction of hERG blockers using machine learning and deep learning approaches / Y. Chen, X. Yu, W. Li [et al.] // Journal of applied toxicology. – 2023. – Vol. 43, N 10. – P. 1462–1475. – DOI: 10.1002/jat.4477.

86. Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations / B. Sharma, V. Chenthamarakshan, A. Dhurandhar [et al.] // Scientific reports. – 2023. – Vol. 13, N 1. – Art. 4908. – DOI: 10.1038/s41598-023-31169-8.

87. Why 90% of clinical drug development fails and how to improve it? / D. Sun, W. Gao, H. Hu, S. Zhou // Acta Pharm Sin B. – 2022. – Vol. 12, N 7. – P. 3049–3062. – DOI: 10.1016/j.apsb.2022.02.002.

88. Kola, I. Can the pharmaceutical industry reduce attrition rates? / I. Kola, J. Landis // Nature reviews. Drug discovery. – 2004. – Vol. 3, N 8. – P. 711–716. – DOI: 10.1038/nrd1470.

89. Lipinski, C. A. Lead- and drug-like compounds: the rule-of-five revolution / C. A. Lipinski // Drug discovery today. Technologies. – 2004. – Vol. 1, N 4. – P. 337–341. – DOI: 10.1016/j.ddtec.2004.11.007.

90. A robust, viable, and resource sparing HPLC-based log P method applied to common drugs / A. L. Coutinho, R. Gristofoletti, F. Wu [et al.] // International journal of pharmaceutics. – 2023. – Vol. 644. – Art. 123325. – DOI: 10.1016/j.ijpharm.2023.123325.

91. Faller, B. Computational approaches to determine drug solubility / B. Faller, P. Ertl // Advanced drug delivery reviews. – 2007. – Vol. 59, N 7. – P. 533–545. – DOI: 10.1016/j.addr.2007.05.005.

92. Comparison of log P and log D correction models trained with public and proprietary data sets / I. Aliagas, A. Gobbi, M. L. Lee, B. D. Sellers // Journal of computer-aided molecular design. – 2022. – Vol. 36, N 4. – P. 253–262. – DOI: 10.1007/s10822-022-00450-9.

93. Win, Z. M. Using machine learning to predict partition coefficient (log P) and distribution coefficient (log D) with molecular descriptors and liquid chromatography retention time / Z. M. Win, A. M. Y. Cheong, W. S. Hopkins // Journal of chemical information and modeling. – 2023. – Vol. 63, N 7. – P. 1906–1913. – DOI: 10.1021/acs.jcim.2c01373.

94. The METLIN small molecule dataset for machine learning-based retention time prediction / X. Domingo-Almenara, C. Guijas, E. Billings [et al.] // Nature communications. – 2019. – Vol. 10, N 1. – Art. 5811. – DOI: 10.1038/s41467-019-13680-7.

95. ChEMBL: a large-scale bioactivity database for drug discovery / A. Gaulton, L. J. Bellis, A. P. Bento [et al.] // Nucleic acids research. – 2012. – Vol. 40, N Database issue. – P. D1100–D1107. – DOI: 10.1093/nar/gkr777.

96. Datta, R. Efficient lipophilicity prediction of molecules employing deep-learning models / R. Datta, D. Das, S. Das // Chemometrics and intelligent laboratory systems. – 2021. – Vol. 213. – Art. 104309. – DOI: 10.1016/j.chemolab.2021.104309.

97. Prasad, S. A deep learning approach for the blind log P prediction in SAMPL6 challenge / S. Prasad, B. R. Brooks // Journal of computer-aided molecular design. – 2020. – Vol. 34, N 5. – P. 535–542. – DOI: 10.1007/s10822-020-00292-3.

98. Cardiac safety assays / J. Heijman, N. Voigt, L. G. Carlsson, D. Dobrev // Current opinion in pharmacology. – 2014. – Vol. 15. – P. 16–21. – DOI: 10.1016/j.coph.2013.11.004.

99. CACHE (Critical Assessment of Computational Hit-finding Experiments): a public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding / S. Ackloo, R. Al-Awar, R. E. Amaro [et al.] // Nature reviews. Chemistry. – 2022. – Vol. 6, N 4. – P. 287–295. – DOI: 10.1038/s41570-022-00363-z.

100. ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries / K. Swanson, P. Walther, J. Leitz [et al.] // Zenodo. – 2023. – 9 Dec. – DOI: 10.5281/zenodo.10372930.

101. MoleculeNet: a benchmark for molecular machine learning / Z. Wu, B. Ramsundar, E. N. Feinberg [et al.] // Chemical science. – 2017. – Vol. 9, N 2. – P. 513–530. – DOI: 10.1039/c7sc02664a.

102. Tox21Challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs / R. Huang, M. Xia, D. T. Nguyen [et al.] // Frontiers in environmental science. – 2016. – Vol. 3. – Art. 85. – DOI: 10.3389/fenvs.2015.00085.

103. ZINC-22 – a free multi-billion-scale database of tangible compounds for ligand discovery / B. I. Tingle, K. G. Tang, M. Castanon [et al.] // Journal of chemical information and modeling. – 2023. – Vol. 63, N 4. – P. 1166–1176. – DOI: 10.1021/acs.jcim.2c01253.

104. Quantum chemistry structures and properties of 134 kilo molecules / R. Ramakrishnan, P. O. Dral, M. Rupp, O. A. von Lilienfeld // Scientific data. – 2014. – Vol. 1. – Art. 140022. – DOI: 10.1038/sdata.2014.22.

105. From computer-aided drug discovery to computer-driven drug discovery / L. Frye, S. Bhat, K. Akinsanya, R. Abel // Drug discovery today. Technologies. – 2021. – Vol. 39. – P. 111–117. – DOI: 10.1016/j.ddtec.2021.08.001.

106. High-throughput screening technology in industrial biotechnology / W. Zeng, L. Guo, S. Xu [et al.] // Trends in biotechnology. – 2020. – Vol. 38, N 8. – P. 888–906. – DOI: 10.1016/j.tibtech.2020.01.001.

107. Sarkar, N. Practical applications of machine learning for anti-infective drug discovery / N. Sarkar, J. M. Stokes // Medicinal Chemistry Reviews / ed.: J. Rudolph, J. J. Bronson. – Washington : ACS, 2023. – Vol. 58. – P. 345–375.

108. Applications of machine learning in microbial natural product drug discovery / A. Arnold, J. Alexander, G. Liu,
J. M. Stokes // Expert opinion on drug discovery. – 2023. – Vol. 18, N 11. – P. 1259–1272. – DOI: 10.1080/17460441.2023.2251400.

109. Artificial intelligence for natural product drug discovery / M. W. Mullowney, K. R. Duncan, S. S. Elsayed [et al.] // Nature reviews. Drug discovery. – 2023. – Vol. 22, N 11. – P. 895–916. – DOI: 10.1038/s41573-023-00774-7.

110. Exploiting machine learning for end-to-end drug discovery and development / S. Ekins, A. C. Puhl, K. M. Zorn [et al.] // Nature materials. – 2019. – Vol. 18, N 5. – P. 435–441. – DOI: 10.1038/s41563-019-0338-z.

111. Designing anticancer peptides by constructive machine learning / F. Grisoni, C. S. Neuhaus, G. Gabernet [et al.] // ChemMedChem. – 2018. – Vol. 13, N 13. – P. 1300–1302. – DOI: 10.1002/cmdc.201800204.

112. Chen, J. xDeep-AcPEP: deep learning method for anticancer peptide activity prediction based on convolutional neural network and multitask learning / J. Chen, H. H. Cheong, S. W. I. Siu // Journal of chemical information and modeling. – 2021. – Vol. 61, N 8. – P. 3789–3803. – DOI: 10.1021/acs.jcim.1c00181.

113. Walker, A. S. A machine learning bioinformatics method to predict biological activity from biosynthetic gene clusters / A. S. Walker, J. Clardy // Journal of chemical information and modeling. – 2021. – Vol. 61, N 6. – P. 2560–2571. – DOI: 10.1021/acs.jcim.0c01304.

114. MELLODDY: cross-pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information / W. Heyndrickx, L. Mervin, T. Morawietz [et al.] // Journal of chemical information and modeling. – 2024. – Vol. 64, N 7. – P. 2331–2344. – DOI: 10.1021/acs.jcim.3c00799.

115. A perspective on explanations of molecular prediction models / G. P. Wellawatte, H. A. Gandhi, A. Seshadri, A. D. White // Journal of chemical theory and computation. – 2023. – Vol. 19, N 8. – P. 2149–2160. – DOI: 10.1021/acs.jctc.2c01235.

116. Crowdsourced mapping of unexplored target space of kinase inhibitors / A. Cichońska, B. Ravikumar, R. J. Allaway [et al.] // Nature communications. – 2021. – Vol. 12, N 1. – Art. 3307. – DOI: 10.1101/2019.12.31.891812.

117. Ketkar, N. Deep Learning with Python / N. Ketkar. – Berkeley, CA : Apress, 2017. – P. 97–111.

REFERENCES

1. Wouters OJ, McKee M, Luyten J. Estimated research and development investment needed to bring a new medicine to market, 2009–2018. JAMA. 2020;323(9):844–53. doi: 10.1001/jama.2020.1166

2. Schenone M, Dančik V, Wagner BK, Clemons PA. Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol. 2013;9(4):232–40. doi: 10.1038/nchembio.1199

3. Ashenden SK. The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry, Cambridge, UK: Academic Press; 2021. 264 p. Chapter 6

4. Smietana K, Siatkowski M, Møller M. Trends in clinical success rates. Nat Rev Drug Discov. 2016;15(6):379–80. doi: 10.1038/nrd.2016.85

5. Harrison RK. Phase II and phase III failures: 2013–2015. Nat Rev Drug Discov. 2016;15(12): 817–8. doi: 10.1038/nrd.2016.184

6. Dowden H, Munro J. Trends in clinical success rates and therapeutic focus. Nat Rev Drug Discov. 2019;18(7):495–6. doi: 10.1038/d41573-019-00074-z

7. Janai J, Güney F, Behl A, Geiger A. Computer vision for autonomous vehicles: problems, datasets and state of the art. Foundations and Trends in Computer Graphics and Vision. 2020;12(1–3):1–308. doi: 10.1561/0600000079

8. Goldberg B, Flemotomos N, Martinez VR, Tanana MJ, Kuo PB, Pace BT, et al. Machine learning and natural language processing in psychotherapy research: alliance as example use case. J Couns Psychol. 2020;67(4):438–48. doi: 10.1037/cou0000382

9. Peterson AA, Liu DR. Small-molecule discovery through DNA-encoded libraries. Nat Rev Drug Discov. 2023;22(9):699–722. doi: 10.1038/s41573-023-00713-6

10. Lim KS, Reidenbach AG, Hua BK, Mason JW, Gerry CJ, Clemans PA, et al. Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss function. J Chem Inf Model. 2022;62(10):2316–31. doi: 10.1021/acs.jcim.2c00041

11. Hou R, Xie C, Gui Y, Li G, Li X. Machine-learning-based data analysis method for cell-based selection of DNA-encoded libraries. ACS Omega. 2023:8(21):19057–71. doi: 10.1021/acsomega.3c02152

12. Van de Sande B, Lee JS, Mutasa-Gottgens E, Naughton B, Bacon W, Manning J, et al. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov. 2023;22(6):496–520. doi: 10.1038/s41573-023-00688-4

13. Yang F, Wang W, Wang F, Fang Y, Tang D, Huang J, et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat Mach Intell. 2022;4(10):852–66. doi: 10.1038/s42256-022-00534-z

14. Chen J, Wang X, Ma A, Wang QE, Liu B, Li L, et al. Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data. Nat Commun. 2022;13(1 Art 6494). doi: 10.1038/s41467-022-34277-7

15. Godinez WJ, Hossain I, Lazic SE, Davies JW, Zhang X. A multi-scale convolutional neural network for phenotyping high-content cellular images. Bioinformatics. 2017;33(13):2010–9. doi: 10.1093/bioinformatics/btx069

16. Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180(4):688–702. doi: 10.1016/j.cell.2020.01.021

17. Coley CW, Barzilay R, Green WH, Jaakkola TS, Jensen KF.  Convolutional embedding of attributed molecular graphs for physical property prediction. J Chem Inf Model. 2017;57(8):1757–72. doi: 10.1021/acs.jcim.6b00601

18. Jin W, Stokes JM, Eastman RT, Itkin Z, Zakharov AV, Collins JJ, et al. Deep learning identifies synergistic drug combinations for treating COVID-19. Proc Natl Acad Sci USA. 2021;118(39 Art e2105070118). doi: 10.1073/pnas.2105070118

19. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463–77. doi: 10.1038/s41573-019-0024-5

20. Rogers D, Hahn M. Extended-connectivity fingerprints. J Chem Inf Model. 2010;50(5):742–54. doi: 10.1021/ci100050t

21. Fernández-De Gortari E, García-Jacas CR, Martinez-Mayorga K, Medina-Franco JL. Database fingerprint (DFP): an approach to represent molecular databases. J Cheminform. 2017;9(1 Art 9). doi: 10.1186/s13321-017-0195-1

22. Liu G, Catacutan DB, Rathod K, Swanson K, Jin W, Mohammed JC, et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol. 2023;19(11):1342–50. doi: 10.1038/s41589-023-01349-8

23. Yang K, Swanson K, Jin W, Coley C, Eiden P, Gao H, et al. Analyzing learned molecular representations for property prediction. J Chem Inf Model. 2019;59(8):3370–88. doi: 10.1021/acs.jcim.9b00237

24. Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, et al. The Drug Repurposing Hub: a next-generation drug library and information resource. Nat Med. 2017;23(4):405–8. doi: 10.1038/nm.4306

25. Wong F, Zheng EJ, Valeri JA, Donghia NM, Anahtar MN, Omori S, et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature. 2024;626(7997):177–85. doi: 10.1038/s41586-023-06887-8

26. Bender BJ, Gahbauer S, Luttens A, Lyu J, Webb CM, Stein RM, et al. A practical guide to large-scale docking. Nat Protoc. 2021;16(10):4799–832. doi: 10.1038/s41596-021-00597-z

27. Gentile F, Yaacoub JC, Gleave J, Fernandez M, Ton AT, Ban F, et al. Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc. 2022;17(3):672–97. doi: 10.1038/s41596-021-00659-2

28. Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov. 2024;23(2):141–55. doi: 10.1038/s41573-023-00832-0

29. Acharya A, Agarwal R, Baker MB, Baudry J, Bhowmik D, Boehm S, et al. Supercomputer-based ensemble docking drug discovery pipeline with application to Covid-19. J Chem Inf Model. 2020;60(12):5832–52. doi: 10.1021/acs.jcim.0c01010

30. Muratov EN, Amaro R, Andrade CH, Brown N, Ekins S, Fourches D, et al. A critical overview of computational approaches employed for COVID-19 drug discovery. Chem Soc Rev. 2021;50(16):9121–51. doi: 10.1039/d0cs01065k

31. Sterling T, Irwin JJ. ZINC 15 – ligand discovery for everyone. J Chem Inf Model. 2015;55(11):2324–37. doi: 10.1021/acs.jcim.5b00559

32. Rossetti GG, Ossorio MA, Rempel S, Kratzel A, Dionellis VS, Barriot S, et al. Non-covalent SARS-CoV-2 Mpro inhibitors developed from in silico screen hits. Sci Rep. 2022;12(1 Art 2505. doi: 10.1038/s41598-022-06306-4

33. Reymond JL. The chemical space project. Acc Chem Res. 2015;48(3):722–30. doi: 10.1021/ar500432k

34. Gómez-Bombarelli R, Wei  JN, Duvenaud D, Hernandez-Lobato JM, Sanchez-Lengeling B, Sheberla D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4(2):268–76. doi: 10.1021/acscentsci.7b00572

35. Anstine DM, Isayev O. Generative models as an emerging paradigm in the chemical sciences. J Am Chem Soc. 2023;145(16):8736–50. doi: 10.1021/jacs.2c13467

36. Jin W, Barzilay R, Jaakkola T. Junction tree variational autoencoder for molecular graph generation. arXiv. 2018 Feb 18. URL: https://arxiv.org/abs/1802.04364 (date of access: 16.03.2026)

37. Godinez WJ, Ma EJ, Chao AT, Pei L, Skewes-Cox P, Canham SM, et al. Design of potent antimalarials with generative chemistry. Nat Mach Intell. 2022;4(2):180–6. doi: 10.1038/s42256-022-00448-w

38. Walters WP, Murcko M. Assessing the impact of generative AI on medicinal chemistry. Nat Biotechnol. 2020;38(2):143–5. doi: 10.1038/s41587-020-0418-2

39. Cesaro A, Bagheri M, Torres M, Wan F, de la Fuente-Nunez C. Deep learning tools to accelerate antibiotic discovery. Expert Opin Drug Discov. 2023;18(11):1245–57. doi: 10.1080/17460441.2023.2250721

40. Rezende DJ, Mohamed S. Variational inference with normalizing flows. In: Bach F, Blei D, editors. International Conference on Machine Learning: proceedings of the 32nd International Conference on Machine Learning. 2015. vol. 37. p. 1530–8

41. Shekhovtsov A, Schlesinger D, Flach B. VAE approximation error: ELBO and exponential families. arXiv. 2021 Feb 18. URL: https://arxiv.org/abs/2102.09310 (date of access: 16.03.2026)

42. Shi C, Xu M, Zhu Z, Zhang W, Zhang M, Tang J. GraphAF: a flow-based autoregressive model for molecular graph generation. arXiv. 2020 Jan 27. URL: https://arxiv.org/abs/2001.09382 (date of access: 16.03.2026)

43. Hoogeboom E, Satorras VG, Vignac C, Welling M. Equivariant diffusion for molecule generation in 3D. In: International Conference on Machine Learning: proceedings of the 39th International Conference on Machine Learning. 2022. vol. 162. p. 8867–87

44. Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci. 1988;28(1):31–6. doi: 10.1021/ci00057a005

45. Grisoni F. Chemical language models for de novo drug design: challenges and opportunities. Curr Opin Struct Biol. 2023;79(Art 102527). doi: 10.1016/j.sbi.2023.102527

46. Flam-Shepherd D, Zhu K, Aspuru-Guzik A. Language models can learn complex molecular distributions. Nat Commun. 2022;13(1 Art 3293). doi: 10.1038/s41467-022-30839-x

47. Skinnider MA, Greg Stacey R, Wishart DS, Foster LJ. Chemical language models enable navigation in sparsely populated chemical space. Nat Mach Intell. 2021;3(9):759–70. doi: 10.1038/s42256-021-00368-1

48. Moret M, Friedrich L, Grisoni F, Merk D, Schneider G. Generative molecular design in low data regimes. Nat Mach Intell. 2020;2(3):171–80. doi: 10.1038/s42256-020-0160-y

49. Ballarotto M, Willems S, Stiller T, Nawa F, Marschner JA, Grisoni F, et al. De novo design of Nurr1 agonists via fragment-augmented generative deep learning in low-data regime. J Med Chem. 2023;66(12):8170–7. doi: 10.1021/acs.jmedchem.3c00485

50. Moret M, Angona IP, Cotos L, Yan S, Atz K, Brunner C, et al. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat Commun. 2023;14(1 Art 114). doi: 10.1038/s41467-022-35692-6

51. Grisoni F, Huisman BJH, Button AL, Moret M, Atz K, Merk D, et al. Combining generative artificial intelligence and on-chip synthesis for de novo drug design. Sci Adv. 2021;7(24 Art eabg3338).  doi: 10.1126/sciadv.abg3338

52. Merk D, Friedrich L, Grisoni F, Schneider G. De novo design of bioactive small molecules by artificial intelligence. Mol Inform. 2018;37(1-2 Art 1700153). doi: 10.1002/minf.201700153

53. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. arXiv. 2017 June 12. URL: https://arxiv.org/abs/1706.03762 (date of access: 16.03.2026)

54. Bagal V, Aggarwal R, Vinod PK, Priyakumar UD. MolGPT: molecular generation using a transformer-decoder model. J Chem Inf Model. 2021;62(9):2064–76. doi: 10.1021/acs.jcim.1c00600

55. Brown N, Fiscato M, Segler MHS, Vaucher AC. GuacaMol: benchmarking models for de novo molecular design. J Chem Inf Model. 2019;59(3):1096–108. doi: 10.1021/acs.jcim.8b00839

56. Polykovskiy D, Zhebrak A, Sanchez-Lengeling B, Golovanov S, Tatanov O, Belyaev S, et al. Molecular sets (MOSES): a benchmarking platform for molecular generation models. Front Pharmacol. 2020;11(Art 565644). doi: 10.3389/fphar.2020.565644

57. Boiko DA, MacKnight R, Kline B, Gomes G. Autonomous chemical research with large language models. Nature. 2023;624:570–8. doi: 10.1038/s41586-023-06792-0

58. Jablonka KM, Schwaller P, Ortega-Guerrero A, Smit B. Leveraging large language models for predictive chemistry. Nat Mach Intell. 2024;6(2):161–9. doi: 10.1038/s42256-023-00788-1

59. Born J, Manica M. Regression Transformer enables concurrent sequence regression and generation for molecular language modeling. Nat Mach Intell. 2023;5(4):432–44. doi: 10.1038/s42256-023-00639-z

60. Frey NC, Soklaski R, Axelrod S, Samsi S, G’omez-Bombarelli R, Coley CW, et al. Neural scaling of deep chemical models. Nat Mach Intell. 2023;5(11):1297–305. doi: 10.1038/s42256-023-00740-3

61. Grechishnikova D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Sci Rep. 2021;11(1 Art 321). doi: 10.1038/s41598-020-79682-4

62. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9. doi: 10.1038/s41586-021-03819-2

63. Stsiapanava A, Xu C, Nishio S, Han L, Yamakawa N, Carroni M, et al. Structure of the decoy module of human glycoprotein 2 and uromodulin and its interaction with bacterial adhesin FimH. Nat Struct Mol Biol. 2022;29(3):190–3. doi: 10.1038/s41594-022-00729-3

64. Liu H, Hong X, Xi J, Menne S, Hu J, Wang JC. Cryo-EM structures of human hepatitis B and woodchuck hepatitis virus small spherical subviral particles. Sci Adv. 2022;8(31 Art eabo4184). doi: 10.1126/sciadv.abo4184

65. Ren F, Ding X, Zheng M, Korzinkin M, Cai X, Zhu W, et al. AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor. Chem Sci. 2023;14(6):1443–52. doi: 10.48550/arXiv.2201.09647

66. Yang Q, Lian X, Syed AAS, Fahira A, Zheng C, Zhu Z, et al. Structural comparison and drug screening of spike proteins of ten SARS-CoV-2 variants. Research (Wash D C). 2022;2022(Art 9781758). doi: 10.34133/2022/9781758

67. Yang Q, Xia D, Syed AAS, Wang Z, Shi Y. Highly accurate protein structure prediction and drug screen of monkeypox virus proteome. J Infect. 2023;86(1):66–117. doi: 10.1016/j.jinf.2022.08.006

68. Ivanenkov YA, Polykovskiy D, Bezrukov D, Zagribelnyy B, Aladinskiy V, Kamya P, et al. Chemistry42: an AI-driven platform for molecular design and optimization. J Chem Inf Model. 2023;63(3):695–701. doi: 10.1021/acs.jcim.2c01191

69. Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021;373(6557):871–6. doi: 10.1126/science.abj8754

70. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TM, Weissig H, et al. The Protein Data Bank. Nucleic Acids Res. 2000;28(1):235–42. doi: 10.1093/nar/28.1.235

71. Van Wart HE, Birkedal-Hansen H. The cysteine switch: a principle of regulation of metalloproteinase activity with potential applicability to the entire matrix metalloproteinase gene family. Proc Natl Acad Sci U S A. 1990;87(14):5578–82. doi: 10.1073/pnas.87.14.5578

72. Michaud JM, Madani A, Fraser JS. A language model beats AlphaFold2 on orphans. Nat Biotechnol. 2022;40(11):1576–7. doi: 10.1038/s41587-022-01466-0

73. Wu R, Ding F, Wang R, Shen R, Zhang X, Luo S, et al. High-resolution de novo structure prediction from primary sequence. bioRxiv. 2022 July 21. doi:10.1101/2022.07.21.500999

74. Fang X, Wang F, Liu L, He J, Lin D, Xiang Y, et al. A method for multiple-sequence-alignment-free protein structure prediction using a protein language model. Nat Mach Intell. 2023;5(10):1087–96. doi: 10.1038/s42256-023-00721-6

75. Madani A, Krause B, Greene ER, Subramanian S, Mohr BP, Holton JM, et al. Large language models generate functional protein sequences across diverse families. Nat Biotechnol. 2023;41(8):1099–106. doi: 10.1038/s41587-022-01618-2

76. Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023;379(6637):1123–30. doi: 10.1126/science.ade2574

77. Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics. 2015;31(6):926–32. doi: 10.1093/bioinformatics/btu739

78. Corso G, Stärk H, Ling B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv. 2022 Oct 4. URL: https://arxiv.org/abs/2210.01776 (date of access: 16.03.2026)

79. Watson JL, Juergens D, Bennett NR, Trippe BL, Yit J, Eisenach HE, et al. De novo design of protein structure and function with RFdiffusion. Nature;2023;620(7976):1089–100. doi: 10.1038/s41586-023-06415-8

80. Theodoris CV, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, Hill MC, et al. Transfer learning enables predictions in network biology. Nature. 2023;618(7965):616–24. doi: 10.1038/s41586-023-06139-9

81. Chaffin M, Papangeli I, Simonson B, Akkad AD, Hill MC, Arduini A, et al. Singlenucleus profiling of human dilated and hypertrophic cardiomyopathy. Nature. 2022;608(7921):174–80. doi: 10.1038/s41586-022-04817-8

82. Hughes JP, Rees SS, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239–49. doi: 10.1111/j.1476-5381.2010.01127.x

83. Goodnow RA. Hit and lead identification: integrated technology-based approaches. Drug Discov Today Technol. 2006;3(4):367–75. doi: 10.1016/j.ddtec.2006.12.009

84. Yang L, Jin C, Yang G, Bing Z, Huang L, Niu Y, et al. Transformer-based deep learning method for optimizing ADMET properties of lead compounds. Phys Chem Chem Phys. 2023;25(3):2377–85. doi: 10.1039/d2cp05332b

85. Chen Y, Yu X, Li W, Tang Y, Liu G. In silico prediction of hERG blockers using machine learning and deep learning approaches. J Appl Toxicol. 2023;43(10):1462–75. doi: 10.1002/jat.4477

86. Sharma B, Chenthamarakshan V, Dhurandhar A, Pereira S, Hendler JA, Dordick JS, et al. Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Sci Rep. 2023;13(1 Art 4908). doi: 10.1038/s41598-023-31169-8

87. Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug development fails and how to improve it? Acta Pharm Sin B. 2022;12(7):3049–62. doi: 10.1016/j.apsb.2022.02.002

88. Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov. 2004;3(8):711–6. doi: 10.1038/nrd1470

89. Lipinski C. A. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. 2004;1(4):337–41. doi: 10.1016/j.ddtec.2004.11.007

90. Coutinho AL, Gristofoletti R, Wu F, Shoyaib AA, Dressman J, Polli JE. A robust, viable, and resource sparing HPLC-based log P method applied to common drugs. Int J Pharm. 2023;644(Art 123325). doi: 10.1016/j.ijpharm.2023.123325

91. Faller B, Ertl P. Computational approaches to determine drug solubility. Adv Drug Deliv Rev. 2007;59(7):533–45. doi: 10.1016/j.addr.2007.05.005

92. Aliagas I, Gobbi A, Lee ML, Sellers BD. Comparison of log P and log D correction models trained with public and proprietary data sets. J Comput Aided Mol Des. 2022;36(4):253–62. doi: 10.1007/s10822-022-00450-9

93. Win ZM, Cheong AMY, Hopkins WS. Using machine learning to predict partition coefficient (log P) and distribution coefficient (log D) with molecular descriptors and liquid chromatography retention time. J Chem Inf Model. 2023;63(7):1906–13. doi: 10.1021/acs.jcim.2c01373

94. Domingo-Almenara X, Guijas C, Billings E, Montenegro-Burke JR, Uritboonthai W, Aisporna AE, et al.  The METLIN small molecule dataset for machine learning-based retention time prediction. Nat Commun. 2019;10(1 Art 5811). doi: 10.1038/s41467-019-13680-7

95. Gaulton A, Bellis LJ, Bento AP, Gambers J, Davies M, Hersey A, et al.  ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40(Database issue):D1100–7. doi: 10.1093/nar/gkr777

96. Datta R, Das D, Das S. Efficient lipophilicity prediction of molecules employing deep-learning models. Chemometr Intell Lab Syst. 2021;213(Art 104309). doi: 10.1016/j.chemolab.2021.104309

97. Prasad S, Brooks BR. A deep learning approach for the blind log P prediction in SAMPL6 challenge. J Comput Aided Mol Des. 2020;34(5):535–42. doi: 10.1007/s10822-020-00292-3

98. Heijman J, Voigt N, Carlsson LG, Dobrev D. Cardiac safety assays. Curr Opin Pharmacol. 2014;15:16–21. doi: 10.1016/j.coph.2013.11.004

99. Ackloo S, Al-Awar R, Amaro RE, Arrowsmith CH, Azevedo H, Batey RA, et al. CACHE (Critical Assessment of Computational Hit-finding Experiments): a public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding. Nat Rev Chem. 2022;6(4):287–95. doi: 10.1038/s41570-022-00363-z

100. Swanson K, Walther P, Leitz J, Mukherjee S, Wu JC, Shivnaraine RV, et al. ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries. Zenodo. 2023 Dec 9. doi: 10.5281/zenodo.10372930

101. Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, et al. MoleculeNet: a benchmark for molecular machine learning. Chem Sci. 2017;9(2):513–30. doi: 10.1039/c7sc02664a

102. Huang R, Xia M, Nguyen DT, Zhao T, Sakamuru S, Zhao J, et al. Tox21Challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Front Environ Sci. 2016;3(Art 85). doi: 10.3389/fenvs.2015.00085

103. Tingle BI, Tang KG, Castanon M, Gutierrez JJ, Khurelbaatar M, Dandarchuluun C, et al. ZINC-22 – a free multi-billion-scale database of tangible compounds for ligand discovery. J Chem Inf Model. 2023;63(4):1166–76. doi: 10.1021/acs.jcim.2c01253

104. Ramakrishnan R, Dral PO, Rupp M, von Lilienfeld OA. Quantum chemistry structures and properties of 134 kilo molecules. Sci Data. 2014;1(Art 140022). doi: 10.1038/sdata.2014.22

105. Frye L, Bhat S, Akinsanya K, Abel R. From computer-aided drug discovery to computer-driven drug discovery. Drug Discov Today Technol. 2021;39:111–7. doi: 10.1016/j.ddtec.2021.08.001

106. Zeng W, Guo L, Xu S, Chen J, Zhou J. High-throughput screening technology in industrial biotechnology. Trends Biotechnol. 2020;38(8):888–906. doi: 10.1016/j.tibtech.2020.01.001

107. Sarkar N, Stokes JM. Practical applications of machine learning for anti-infective drug discovery. In: Rudolph J, Bronson JJ, editors. Medicinal Chemistry Reviews. Washington, USA: ACS; 2023. vol. 58. p. 345–75

108. Arnold A, Alexander J, Liu G, Stokes JM. Applications of machine learning in microbial natural product drug discovery. Expert Opin Drug Discov. 2023;18(11):1259–72. doi: 10.1080/17460441.2023.2251400

109. Mullowney MW, Duncan KR, Elsayed SS, Garg N, van der Hooft JJJ, Martin NI, et al. Artificial intelligence for natural product drug discovery. Nat Rev Drug Discov. 2023;22(11):895–916. doi: 10.1038/s41573-023-00774-7

110. Ekins S, Puhl AC, Zorn KM, Lane TR, Russo DP, Klein JJ, et al. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater. 2019;18(5):435–41. doi: 10.1038/s41563-019-0338-z

111. Grisoni F, Neuhaus CS, Gabernet G, Muller AT, Hiss JA, Schneider G. Designing anticancer peptides by constructive machine learning. ChemMedChem. 2018;13(13):1300–2. doi: 10.1002/cmdc.201800204

112. Chen J, Cheong HH, S. Siu SWI. xDeep-AcPEP: deep learning method for anticancer peptide activity prediction based on convolutional neural network and multitask learning. J Chem Inf Model. 2021;61(8):3789–803. doi: 10.1021/acs.jcim.1c00181

113. Walker AS, Clardy J. A machine learning bioinformatics method to predict biological activity from biosynthetic gene clusters. J Chem Inf Model. 2021;61(6):2560–71. doi: 10.1021/acs.jcim.0c01304

114. Heyndrickx W, Mervin L, Morawietz T, Sturm N, Friedrich L, Zalewski A, et al. MELLODDY: cross-pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information. J Chem Inf Model. 2024;64(7):2331–44. doi: 10.1021/acs.jcim.3c00799

115. Wellawatte GP, Gandhi HA, Seshadri A, White AD. A perspective on explanations of molecular prediction models. J Chem Theory Comput. 2023;19(8):2149–60. doi: 10.1021/acs.jctc.2c01235

116. Cichońska A, Ravikumar B, Allaway RJ, Wan F, Park S, Isayev O, et al. Crowdsourced mapping of unexplored target space of kinase inhibitors. Nat Commun. 2021;12(1 Art 3307). doi: 10.1101/2019.12.31.891812

117. Ketkar N. Deep Learning with Python. Berkeley, CA: Apress; 2017. p. 97–111

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УО «Витебский государственный ордена

Дружбы народов медицинский университет»,

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токсикологической химии,

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Дергачёва Ж. М.

Поступила 23.03.2026 г.

УДК 372.881.1:615.1]:159.9
DOI: https://doi.org/10.52540/2074-9457.2026.1.65
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В. В. Царенкова, С. И. Шпановская

Формирование иноязычной коммуникативной компетенции у студентов фармацевтического профиля на основе деятельностного подхода 

Белорусский государственный технологический университет,  г. Минск, Республика Беларусь

В статье рассматриваются особенности применения деятельностно-коммуникативного подхода в методике преподавания иностранного языка в условиях современного образовательного пространства. Актуальность темы обусловлена смещением целей языкового образования в сторону формирования способности обучающихся использовать иностранный язык как средство реального общения, а не только как объект усвоения языковых правил. В работе обобщены основные теоретические положения деятельностно-коммуникативного подхода и проанализированы практические аспекты его реализации в учебном процессе. Особое внимание уделяется характеристике коммуникативно-ориентированных заданий, использованию аутентичных материалов и организации речевого взаимодействия обучающихся. В качестве показателей эффективности рассматриваются уровень речевой активности, степень вовлеченности в учебную деятельность, способность к спонтанному высказыванию и мотивация к изучению иностранного языка.

Показано, что применение деятельностно-коммуникативного подхода способствует повышению практической направленности обучения и созданию условий для формирования коммуникативной компетенции обучающихся. Материалы статьи могут быть использованы в практике преподавания иностранных языков, а также при разработке учебно-методических материалов. В статье подчеркивается важность создания поддерживающей и стимулирующей образовательной среды, способствующей снижению языкового барьера, и развитию уверенности в использовании иностранного языка.

Ключевые слова: методика преподавания, иностранный язык, деятельностно-коммуникативный подход, речевая деятельность, обучение.

SUMMARY

V. V. Tsarenkova, S. I. Shpanovskaya

FACILITIES FOR USING COMMUNICATIVE LANGUAGE TEACHING APPROACH FOR THE STUDENTS OF THE PHARMACEUTICAL FACULTY

This article examines specifics of application of the performance-communicative approach in modern foreign language teaching methodology. The relevance of this topic is determined by a shift in the language educational goals for the students to form skills in using a foreign language as a means of real communication rather than a subject of acquiring foreign language rules. The paper outlines the main theoretical foundations of the performance-communicative approach and evaluates practical aspects of its application in the educational process. Special attention is given to the characteristics of communicative-oriented tasks, integration of authentic materials and organization of student communicative interaction. Effectiveness indicators are measured by students' speech activity, engagement levels in educational acti-vity, spontaneity in expression and motivation for learning a foreign language.

It is stated that application of the performance-communicative approach enhances the practical focus of education and fosters proper conditions to form students’ communicative competence. These findings offer valuable insights in the practice of teaching foreign languages as well as developing teaching materials. Furthermore, the article highlights the necessity of creating supportive and stimulating environment contributing to reduction in a language barrier and building confidence while using a foreign language.

Keywords: foreign language teaching methodology, foreign language, performance-communicative approach, speech activity, education.

ЛИТЕРАТУРА

1. Герасименко, Е. Е. Особенности коммуникативной методики преподавания иностранных языков / Е. Е Герасименко // Вестник Шадринского государственного педагогического университета. – 2021. – № 1. – С. 35–37. – DOI: 10.52772/25420291_2021_1_35.

2. Гарипова, А. Н. Коммуникативный подход как эффективное средство формирования межкультурной компетенции студентов спортивного профиля / А. Н. Гарипова, З. И. Павицкая // Наука и спорт: современные тенденции. – 2024. – Т. 12, № S2. – С. 194–199. – DOI: 10.36028/2308-8826-2024-12-S2-194-199.

3. Al-Amri, H. M. Digital storytelling as a communicative language teaching based method in EFL classrooms / H. M. Al-Amri // Arab World English Journal. – 2020. – Vol. 11, N 1. – P. 270–281. – DOI: 10.24093/awej/vol11no1.20.

4. Касумова, Г. А. Аутентичный аудиовизуальный материал как инструмент развития коммуникативных навыков у студентов неязыковых вузов / Г. А. Касумова, А. А. Васильева // Педагогическое образование в России. – 2023. – № 5. – С. 117–125.

5. Скачинская, И. А. Роль и значение коммуникативных ситуаций в обучении говорению на иностранном языке в неязыковом вузе / И. А. Скачинская. – Минск : Белорус. гос. мед. ун-т, 2025. – URL: https://www.bsmu.by/upload/docs/kafedri/k_inostr_yaz/2025-2/%D0%A1%D0%BA%D0%B0%D1%87%D0%B8%D0%BD%D1%81%D0%BA%D0%B0%D1%8F_%D0%98%D0%90_%D1%81%D1%82%D0%B0%D1%82%D1%8C%D1%8F.pdf (дата обращения: 10.12.2025).

6. An evaluative study for communicative language teaching (CLT) on online teaching and learning in higher education: Indonesian and Malaysian university context / M. Taridi, Risnita, M. F. M. Yaakob, M. Khairani // Education and information technologies. – 2024. – Vol. 29, N 9. – P. 10611–10647. – DOI: 10.1007/s10639-023-12221-6.

7. Перевышко, А. И. Коммуникативный подход в обучении иностранному языку / А. И. Перевышко, Е. В. Юшкевич // Преподавание иностранных языков в поликультурном мире: традиции, инновации, перспективы : сб. ст. III Междунар. науч.-практ. конф., г. Минск, 25 марта 2021 г. – Минск : Белорус. гос. пед. ун-т им. М. Танка, 2021. – С. 175–178. – URL: https://libeldoc.bsuir.by/bitstream/123456789/44929/1/Perevyshko_Kommunikativnyy1.pdf (дата обращения: 10.12.2025).

8. Teng, M. F. Communicative and task-based language teaching: where are we now? / M. F. Teng, E. Z. Wen, D. Li // The Language Learning Journal. – 2025. – Vol. 53, N 6. – P. 1–9. – DOI: 10.1080/09571736.2025.2572673.

9. Qasserras, L. Systematic review of communicative language teaching (CLT) in language education: a balanced perspective / L. Qasserras // European journal of education and pedagogy. – 2023. – Vol. 4, N 6. – P. 17–23. – DOI:  10.24018/ejedu.2023.4.6.763.

10. Zhou, D. The role of teacher–student developmental relationships in linking communicative language teaching and communicative competence / D. Zhou, Q. Li // Frontiers in psycho-logy. – 2025. – Vol. 16. – Art. 16354280. – DOI: 10.3389/fpsyg.2025.1635428.

11. Зимняя, И. А. Педагогическая психология : учеб. для вузов / И. А. Зимняя. – 2-е изд., доп., испр. и перераб. – Москва : Логос, 2005. – 384 с.

12. Пассов, Е. И. Коммуникативный метод обучения иноязычному говорению / Е. И. Пассов. – 2-е изд. – Москва : Просвещение, 1991. – 223 с.

REFERENCES

1. Gerasimenko EE. Features of the communicative methodology of teaching foreign languages. Vestnik Shadrinskogo gosudarstvennogo pedagogicheskogo universiteta. 2021;(1):35–7. doi: 10.52772/25420291_2021_1_35. (In Russ.)

2. Garipova AN, Pavitskaia ZI. Communicative approach as an effective means of developing intercultural competence in sports students. Nauka i sport: sovremennye tendentsii. 2024;12(S2):194–9. doi: 10.36028/2308-8826-2024-12-S2-194-199. (In Russ.)

3. Al-Amri HM. Digital storytelling as a communicative language teaching based method in EFL classrooms. Arab World English Journal. 2020;11(1):270–81. doi: 10.24093/awej/vol11no1.20

4. Kasumova GA, Vasil'eva AA. Authentic audiovisual material as a tool for developing communication skills in students of non-linguistic universities. Pedagogicheskoe obrazovanie v Rossii. 2023;(5):117–25. (In Russ.)

5. Skachinskaia IA. The role and importance of communicative situations in teaching speaking in a foreign language at a non-linguistic university. Minsk, RB: Belorus. gos. med. un-t; 2025. URL: https://www.bsmu.by/upload/docs/kafedri/k_inostr_yaz/2025-2/%D0%A1%D0%BA%D0%B0%D1%87%D0%B8%D0%BD%D1%81%D0%BA%D0%B0%D1%8F_%D0%98%D0%90_%D1%81%D1%82%D0%B0%D1%82%D1%8C%D1%8F.pdf (data ob-rashcheniia: 10.12.2025). (In Russ.)

6. An evaluative study for communicative language teaching (CLT) on online teaching and learning in higher education: Indonesian and Malaysian university context / M. Taridi, Risnita, M. F. M. Yaakob, M. Khairani // Education and information technologies. – 2024. – Vol. 29, N 9. – P. 10611–10647. – DOI: 10.1007/s10639-023-12221-6.

7. Perevyshko AI, Iushkevich EV. Communicative approach in teaching foreign languages. V: Prepodavanie inostrannykh iazykov v polikul'turnom mire: traditsii, innovatsii, perspektivy: sb st III Mezhdunar nauch-prakt konf, Minsk, 25 marta 2021 g. Minsk, RB: Belorus gos ped un-t im. M. Tanka; 2021. s. 175–8. URL: https://libeldoc.bsuir.by/bitstream/123456789/44929/1/Perevyshko_Kommunikativnyy1.pdf (data obrashcheniia: 10.12.2025). (In Russ.)

8. Teng MF, Wen EZ, Li D. Communicative and task-based language teaching: where are we now? The Language Learning Journal. 2025;53(6):1–9. doi: 10.1080/09571736.2025.2572673

9. Qasserras L. Systematic review of communicative language teaching (CLT) in language education: a balanced perspective. European Journal of Education and Pedagogy. 2023;4(6):17–23. doi:  10.24018/ejedu.2023.4.6.763

10. Zhou D, Li Q. The role of teacher–student developmental relationships in linking communicative language teaching and communicative competence. Front Psychol. 2025;16(Art 1635428). doi: 10.3389/fpsyg.2025.1635428

11. Zimniaia IA. Educational psychology: ucheb dlia vuzov. 2-e izd, dop, ispr i pererab. Moskva, RF: Logos; 2005. 384 s. (In Russ.)

12. Passov EI. Communicative method of teaching foreign language speaking. 2-e izd. Moskva, RF: Prosveshchenie; 1991. 223 s. (In Russ.)

Адрес для корреспонденции:

220006, Республика Беларусь,

г. Минск, ул. Свердлова, 13а,

Белорусский государственный

технологический университет,

тел. (+375 17) 226 10 82,

alexej0779@yandex.ru, vvalery@inbox.ru,

Царенкова В. В.

Поступила 10.02.2026 г.

УДК 614.2:615.1]:331.363
DOI: https://doi.org/10.52540/2074-9457.2026.1.51
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Е. В. Игнатьева, Г. А. Хуткина, Т. А. Дорофеева

ОПЫТ ОРГАНИЗАЦИИ И ПРОВЕДЕНИЯ ПЕРЕПОДГОТОВКИ  ПО СПЕЦИАЛЬНОСТИ «УПРАВЛЕНИЕ И ЭКОНОМИКА В ФАРМАЦИИ»

Витебский государственный ордена Дружбы народов медицинский университет,  г. Витебск, Республика Беларусь

С целью совершенствования образовательного процесса и повышения качества подготовки фармацевтических специалистов проведено исследование опыта организации переподготовки по специальности «Управление и экономика в фармации» в учреждении образования «Витебский государственный ордена Дружбы народов медицинский университет». Исследование выполнено путем анкетирования выпускников факультета повышения квалификации и переподготовки кадров, освоивших образовательную программу переподготовки руководящих работников и специалистов, имеющих высшее образование, в 2023 и 2024 г. Результаты анкетирования первого выпуска учтены при разработке проекта нового образовательного стандарта и примерного учебного плана переподготовки по специальности «Управление и экономика в фармации». Показана удовлетворенность слушателей тематикой и объемом учебных часов по разделам обновленного учебного плана и полное его соответствие получаемой специальности.

Ключевые слова: переподготовка, управление и экономика в фармации, образовательный стандарт, фармацевтический специалист, анкетирование.

SUMMARY

A. V. Ihnatsyeva, G. A. Hutkina, T. A. Dorofeeva

EXPERIENCE IN ORGANIZING AND CONDUCTING RETRAINING IN THE SPECIALTY “MANAGEMENT AND ECONOMICS IN PHARMACY”

In order to improve the educational process and enhance the quality of training for pharmaceutical specialists a study was conducted on the experience of organizing retraining in the specialty “Management and Economics in Pharmacy” at Vitebsk State Order of Peoples’ Friendship Medical University. The study was conducted by making a questionnaire among the graduates of the Advanced Training and Retraining Faculty who completed the educational program for executives and specialists, having higher education, in 2023 and 2024. The results of the questionnaire for the first graduates were taken into account when developing a new educational standard and an approximate curriculum for retraining in the specialty “Management and Economics in Pharmacy.” The learners’ satisfaction with the subject matter and the amount of academic hours in the sections of the updated curriculum and its full compliance with the specialty being studied is demonstrated.

Keywords: retraining, management and economics in pharmacy, educational standard, pharmaceutical specialist, questionnaire.

ЛИТЕРАТУРА

1. О требованиях к занятию должностей служащих медицинских, фармацевтических работников : постановление М-ва здравоохранения Респ. Беларусь от 31 мая 2021 г. № 72 // Национальный правовой Интернет-портал Республики Беларусь. – URL: https://pravo.by/document/?guid=12551&p0=W22136859 (дата обращения: 01.12.2025)

2. Об утверждении образовательных стандартов переподготовки руководящих работников и специалистов по специальностям переподготовки : постановление М-ва образования Респ. Беларусь от 10 дек. 2020 г. 299 // Национальный правовой Интернет-портал Республики Беларусь. – URL: https://pravo.by/document/?guid=12551&p0=W22136289p&p1=1 (дата обращения: 01.12.2025)

3. Об утверждении типовых учебных планов по специальностям переподготовки : постановление М-ва образования Респ. Беларусь от 10 дек. 2020 г. № 300 // Национальный правовой Интернет-портал Республики Беларусь. – URL: https://pravo.by/document/?guid=12551&p0=W22136246p&p1=1 (дата обращения: 01.12.2025)

4. Об утверждении образовательных стандартов переподготовки руководящих работников и специалистов по специальностям переподготовки : постановление М-ва здравоохранения Респ. Беларусь от 2 окт. 2023 г. № 157 // Национальный правовой Интернет-портал Республики Беларусь. – URL: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://minzdrav.gov.by/upload/lcfiles/%D0%BF%D0%BE%D1%81%D1%82%D0%B0%D0%BD%D0%BE%D0%B2%D0%BB%D0%B5%D0%BD%D0%B8%D0%B5_%D0%9C%D0%97_2023_157.pdf (дата обращения: 01.12.2025)

5. Примерный учебный план по специальности переподготовки 9-09-00-12-01 «Управление и экономика в фармации». – URL:  https://nihe.by/images/9-09-0912-01_%D0%A3%D0%BF%D1%80%D0%B0%D0%B2%D0%BB%D0%B5%D0%BD%D0%B8%D0%B5_%D0%B8_%D1%8D%D0%BA%D0%BE%D0%BD%D0%BE%D0%BC%D0%B8%D0%BA%D0%B0_%D0%B2_%D1%84%D0%B0%D1%80%D0%BC%D0%B0%D1%86%D0%B8%D0%B8.pdf (дата обращения: 01.12.2025)

REFERENCES

1. On the requirements for filling positions of medical and pharmaceutical workers: postanovlenie M-va zdravookhraneniia Resp Belarus' ot 31 maia 2021 g № 72. Natsional'nyi pravovoi Internet-portal Respubliki Belarus'. URL: https://pravo.by/document/?guid=12551&p0=W22136859 (data obrashcheniia: 01.12.2025). (In Russ.)

2. On approval of educational standards for retraining of executives and specialists in retraining specialties: postanovlenie M-va obrazovaniia Resp Belarus' ot 10 dek 2020 g 299. Natsional'nyi pravovoi Internet-portal Respubliki Belarus'. URL: https://pravo.by/document/?guid=12551&p0=W22136289p&p1=1 (data obrashcheniia: 01.12.2025 ). (In Russ.)

3. On approval of standard curricula for retraining specialties: postanovlenie M-va obrazovaniia Resp Belarus' ot 10 dek 2020 g № 300. Natsional'nyi pravovoi Internet-portal Respubliki Belarus'. URL: https://pravo.by/document/?guid=12551&p0=W22136246p&p1=1 (data obrashcheniia: 01.12.2025). (In Russ.)

4. On approval of educational standards for retraining of executives and specialists in retraining specialties: postanovlenie M-va zdravookhraneniia Resp Belarus' ot 2 okt 2023 g № 157. Natsional'nyi pravovoi Internet-portal Respubliki Belarus'. URL: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://minzdrav.gov.by/upload/lcfiles/%D0%BF%D0%BE%D1%81%D1%82%D0%B0%D0%BD%D0%BE%D0%B2%D0%BB%D0%B5%D0%BD%D0%B8%D0%B5_%D0%9C%D0%97_2023_157.pdf (data obrashcheniia: 01.12.2025). (In Russ.)

5. Sample curriculum for the retraining specialty 9-09-00-12-01 “Management and Economics in Pharmacy”. URL:  https://nihe.by/images/9-09-0912-01_%D0%A3%D0%BF%D1%80%D0%B0%D0%B2%D0%BB%D0%B5%D0%BD%D0%B8%D0%B5_%D0%B8_%D1%8D%D0%BA%D0%BE%D0%BD%D0%BE%D0%BC%D0%B8%D0%BA%D0%B0_%D0%B2_%D1%84%D0%B0%D1%80%D0%BC%D0%B0%D1%86%D0%B8%D0%B8.pdf (data obrashcheniia: 01.12.2025). (In Russ.)

Адрес для корреспонденции:

210009, Республика Беларусь,

г. Витебск, пр. Фрунзе, 27,

УО «Витебский государственный ордена Дружбы народов медицинский университет»,

кафедра фармации ФПК и ПК,

тел. раб.: 8 (0212) 60-14-08,

е-mail: kafedra.farmacii.fpkipk@gmail.com,

Хуткина Г. А.

Поступила 16.02.2026 г.

УДК 615.22:615.015.4
DOI: https://doi.org/10.52540/2074-9457.2026.1.43
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Л. И. Покачайло

БИТЕНЗА И ПРЕСТАНС: ИССЛЕДОВАНИЕ БИОЭКВИВАЛЕНТНОСТИ ПРИ ОДНОКРАТНОМ ПЕРОРАЛЬНОМ ПРИЕМЕ

Производственное республиканское унитарное предприятие «Минскинтеркапс», г. Минск, Республика Беларусь

Статья посвящена экспериментальному доказательству биологической эквивалентности двух лекарственных препаратов, содержащих периндоприл и амлодипин – Битенза, капсулы 8 мг/10 мг, производства УП «Минскинтеркапс», Республика Беларусь, и Престанс, таблетки 10 мг/10 мг, производства Ле Лаборатуар Сервье, Франция. Испытание проведено в условиях однократного перорального приема здоровыми добровольцами натощак. В результате проведенного исследования установлена биологическая эквивалентность лекарственных препаратов – Битенза и Престанс. Для периндоприла, периндоприлата и амлодипина выполняются критерии сопоставимости показателей максимальной концентрации в сыворотке крови после приема препарата и площади под фармакокинетической кривой. 90% доверительные интервалы для ожидаемых средних значений основных фармакокинетических параметров AUC и Cmax, полностью входят в диапазон биоэквивалентности 80,00–125,00%. Профиль безопасности обоих препаратов оценивается как сопоставимый и удовлетворительный.

Ключевые слова: Битенза, Престанс, амлодипин, периндоприл, периндоприлат, биоэквивалентность.

SUMMARY

L. I. Pokachailo

BITENZA AND PRESTANCE: BIOEQUIVALENCE STUDY IN A SINGLE ORAL INTAKE

The article is devoted to the experimental proof of biological equivalence of two drugs containing perindopril and amlodipine – Bitenza, 8/10 mg capsules manufactured by UE Minskintercaps, the Republic of Belarus, and Prestance, 10/10 mg tablets manufactured by Le Laboratoire Servier, France. The test was performed under conditions of a single oral intake by healthy volunteers on an empty stomach. As a result of the conducted research biological equivalence of medicinal products - Bitenza and Prestance – was established. For perindopril, perindoprilate and amlodipine, comparability criteria of maximum serum concentration after the drug intake and the area under the pharmacokinetic curve are followed. 90% confidence intervals for the expected mean values of the main pharmacokinetic parameters AUC and Cmax are fully within the bioequivalence range of 80.00–125.00%. The safety profile of both drugs is assessed as comparable and satisfactory.

Keywords: Bitenza, Prestancе, amlodi-pine, perindopril, perindoprilate, bioequivalence.

ЛИТЕРАТУРА

1. 29 сентября – Всемирный День сердца. – URL: https://minzdrav.gov.by/ru/novoe-na-sayte/29-sentyabrya-vsemirnyy-den-serdtsa-/?ysclid=mprcxeaqvo350699296 (дата обращения: 16.03.2026). 

2. Эпидемиология болезней сердца руководство к действию. – URL: http://www.minzchie.by/menucatobrazlife/2017-10-24 -17-46-18/4533-2021-10-07-06-44-19.html (дата обращения: 16.03.2026). 

3. Общая характеристика лекарственного препарата : [Престанс]. – URL: https://www.rceth.by/NDfiles/pdfeaec/000575/OHLP.pdf (дата обращения: 16.03.2026).

4. Единый реестр зарегистрированных лекарственных средств Евразийского экономического союза. Битенза. – URL: https://pharma.eaeunion.org/pharma/registers/26/ru/register (дата обращения: 16.03.2026).

5. Романова, И. С. Вопросы лекарственного взаимодействия в кардиологической практике. Акцент на ингибиторы ангиотензинпревращающего фермента и блокаторы кальциевых каналов / И. С. Романова // Рецепт. – 2025. – Т. 28, № 2. – С. 168–175. – DOI: 10.34883/PI.2025.28.2.007.

6. Clinical synergy of perindopril and calcium-channel blocker in the prevention of cardiac events and mortality in patients with coronary artery disease. Post hoc analysis of the EUROPA study / M. E. Bertrand, R. Ferrari, W. J. Remme [et al.] // American heart journal.  – 2010. – Vol. 159, N 5. – Р. 795–802. – DOI: 10.1016/j.ahj.2009.12.042.

7. Леонова, М. В. Клиническая фармакология комбинации ингибиторов ангиотензин-превращающего фермента и антагонистов кальция: преимущества комбинации периндоприла аргинина и амлодипина / M.В. Леонова // Кардиоваскулярная терапия и профилактика. – 2010. – Т. 9, № 8. – С. 55–62.

8. Об утверждении Правил проведения исследований биоэквивалентности лекарственных препаратов в рамках Евразийского экономического союза : решение Совета Евраз. экон. комис. от 3 нояб. 2016 г. № 85. – URL: https://docs.eaeunion.org/upload/iblock/e38/imw0cudh94d4snl49dcfj1649pnmdhc1/cncd_21112016_85_doc.pdf (дата обращения: 16.03.2026).

9. Об утверждении Правил надлежащей клинической практики Евразийского экономического союза : решение Совета Евраз. экон. комис. от 3 нояб. 2016 г. № 79. – URL: https://docs.eaeunion.org/upload/iblock/e21/m3u4481wsjaquchmxckqythcojef9d3f/cncd_21112016_79_doc.pdf (дата обращения: 16.03.2026).

10. Об утверждении Правил надлежащей производственной практики Евразийского экономического союза : решение Совета Евраз. экон. комис. от 3 нояб. 2016 г. № 77. – URL: https://docs.eaeunion.org/upload/iblock/bd9/ga9qmu732f3sef7duto3zomhjfpxr0w1/cncd_21112016_77_doc.pdf (дата обращения: 16.03.2026).

11. Об утверждении Правил проведения исследований биоэквивалентности лекарственных препаратов в рамках Евразийского экономического союза. Приложение № 6. Требования к валидации биоаналитических методик испытаний и анализу исследуемых биологических образцов : решение Совета Евраз. экон. комис. от 3 нояб. 2016 г. № 85. – URL: https://docs.eaeunion.org/upload/iblock/e38/imw0cudh94d4snl49dcfj1649pnmdhc1/cncd_21112016_85_doc.pdf (дата обращения: 16.03.2026).

12. Guideline on Bioanalytical Method Validation / European Medicines Agency, Committee for Medicinal Products for Human Use (CHMP). – URL: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-bioanalytical-method-validation_en.pdf/ (date of access: 16.03.2026).

REFERENCES

1. September 29 is World Heart Day. URL: https://minzdrav.gov.by/ru/novoe-na-sayte/29-sentyabrya-vsemirnyy-den-serdtsa-/?ysclid=mprcxeaqvo350699296 (data obrashcheniia: 16.03.2026). (In Russ.)

2. Epidemiology of heart disease: a guide to action. URL: http://www.minzchie.by/menucatobrazli fe/2017-10-24-17-46-18/4533-2021-10-07-06-44-19.html (data obrashcheniia: 16.03.2026). (In Russ.)

3. General characteristics of the drug: [Prestans]. URL: https://www.rceth.by/NDfiles/pdfeaec/000575/OHLP.pdf (data obrashcheniia: 16.03.2026). (In Russ.)

4. Unified Register of Registered Medicines of the Eurasian Economic Union. Bitenza. URL: https://pharma.eaeunion.org/pharma/registers/26/ru/register (data obrashcheniia: 16.03.2026). (In Russ.)

5. Romanova IS. Drug Interactions in Cardiology Practice: Focus on Angiotensin-Converting Enzyme Inhibitors and Calcium Channel Blockers. Retsept. 2025;28(2):168–75. doi: 10.34883/PI.2025.28.2.007. (In Russ.)

6. Bertrand ME, Ferrari R, Remme WJ, Simoons ML, Deckers JW, Fox KM. Clinical synergy of perindopril and calcium-channel blocker in the prevention of cardiac events and mortality in patients with coronary artery disease. Post hoc analysis of the EUROPA study. Am Heart J. 2010;159(5):795–802. doi: 10.1016/j.ahj.2009.12.042

7. Leonova MV. Clinical pharmacology of a combination of angiotensin-converting enzyme inhibitors and calcium antagonists: advantages of a combination of perindopril arginine and amlodipine. Kardiovaskuliarnaia terapiia i profilaktika. 2010;9(8):55–62. (In Russ.)

8. On approval of the Rules for conducting bioequivalence studies of medicinal products within the Eurasian Economic Union: reshenie Soveta Evraz ekon komis ot 3 noiab 2016 g № 85. URL: https://docs.eaeunion.org/upload/iblock/e38/imw0cudh94d4snl49dcfj1649pnmdhc1/cncd_21112016_85_doc.pdf (data obrashcheniia: 16.03.2026). (In Russ.)

9. On approval of the Rules of Good Clinical Practice of the Eurasian Economic Union: reshenie Soveta Evraz ekon komis ot 3 noiab 2016 g № 79. URL: https://docs.eaeunion.org/upload/iblock/e21/m3u4481wsjaquchmxckqythcojef9d3f/cncd_21112016_79_doc.pdf (data obrashcheniia: 16.03.2026). (In Russ.)

10. On approval of the Rules of Good Manufacturing Practice of the Eurasian Economic Union: reshenie Soveta Evraz ekon komis ot 3 noiab 2016 g № 77. URL: https://docs.eaeunion.org/upload/iblock/bd9/ga9qmu732f3sef7duto3zomhjfpxr0w1/cncd_21112016_77_doc.pdf (data obrashcheniia: 16.03.2026). (In Russ.)

11. On Approval of the Rules for Conducting Bioequivalence Studies of Medicinal Products within the Eurasian Economic Union. Appendix N 6. Requirements for the Validation of Bioanalytical Test Methods and Analysis of Biological Samples: reshenie Soveta Evraz ekon komis ot 3 noiab 2016 g № 85. URL: https://docs.eaeunion.org/upload/iblock/e38/imw0cudh94d4snl49dcfj1649pnmdhc1/cncd_21112016_85_doc.pdf (data obrashcheniia: 16.03.2026). (In Russ.)

12. European Medicines Agency, Committee for Medicinal Products for Human Use (CHMP). Guideline on Bioanalytical Method Validation. URL: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-bioanalytical-method-validation_en.pdf/ (date of access: 16.03.2026)

Адрес для корреспонденции:

220075, Республика Беларусь,

г. Минск, ул. Инженерная, 26,

УП «Минскинтеркапс»,

тел/факс: +375 (17) 230-16-82

e-mail: pokachailo@mic.by,

Покачайло Л. И.

Поступила 23.03.2026 г.