Оценочные функции моделей молекулярного докинга белков в автоматизированных системах обработки и анализа биоинформационных данных
https://doi.org/10.17586/2226-1494-2026-26-4-683-694
Аннотация
Представлена обобщенная интегрированная классификация оценочных (скоринговых функций) молекулярного докинга моделей взаимодействия белков. Приведено описание различных видов скоринговых функций. Рассмотрены отдельные релевантные вопросы биофармацевтики, медицинской кибернетики, вычислительной биологии и биофизики, прикладной математики. Работа может быть интересна ученым различных специальностей, инженерам-программистам, разрабатывающим автоматизированные системы обработки биоинформации; преподавателям-лекторам по данной тематике; магистрантам и аспирантам по направлениям подготовки: биоинформатика, системный анализ, метрология и приборостроение, системы медицинского назначения и теоретические основы информатики.
Ключевые слова
Об авторах
М. В. СпринджукБеларусь
Спринджук Матвей Владимирович — кандидат технических наук, старший научный сотрудник
Минск, 220072
В. В. Кукарцев
Россия
Кукарцев Владислав Викторович — кандидат технических наук, доцент; ведущий научный сотрудник
sc 57394094900
Москва, 127434
Москва,105005
А. В. Ноздрачева
Россия
Ноздрачева Анна Валерьевна — кандидат медицинских наук, руководитель лабораторией
sc 57200130931
Москва, 105005
А. П. Кончиц
Беларусь
Кончиц Андрей Петрович — кандидат биологических наук, ведущий научный сотрудник
Гомель, 246050
А. С. Владыко
Беларусь
Владыко Александр Станиславович — доктор медицинских наук, внештатный научный консультант
Минск, 220114
В. И. Берник
Россия
Берник Василий Иванович — доктор физико-математических наук, профессор, главный научный сотрудник
sc 14064737000
Минск,220072
Е. И. Сукач
Беларусь
Сукач Елена Ивановна — кандидат технических наук, доцент, доцент
Гомель, 246050
Список литературы
1. Ugbaja S.C., Mushebenge A.G., Kumalo H., Ngcobo M., Gqaleni N. Potential benefits of in silico methods: a promising alternative in natural compound’s drug discovery and repurposing for HBV therapy // Pharmaceuticals. 2025. V. 18. N 3. P. 419. do i: 10.3390/ph18030419
2. Tanoli Z., Fernandez-Torras A., Ozcan U.O., Kushnir A., Nader K.M., Gadiya Y., et al. Computational drug repurposing: approaches, evaluation of in silico resources and case studies // Nature Reviews Drug Discovery. 2025. V. 24. N 7. P. 521–542. doi: 10.1038/s41573-025-01164-x
3. Kalsi P., Jain P., Goyal G., Sharma H. In silico drug repurposing approach to predict most effective HAART for HIV drug resistance variants prevalent in the Indian HIV-positive population // AIDS Reviews. 2024. V. 26. N 3. P. 93–101. doi: 10.24875/AIDSRev.24000010
4. Hussein D., Almatrafi A., Gomaa M., Alhowsawi A., Almustafa S., Alsaihaty H., et al.. In silico drug repurposing using molecular docking and dynamics to target the protein interaction between the SARSCoV-2 S-glycoprotein and the ACE2 receptor // F1000Research. 2023. V. 12. P. 1452. doi: 10.12688/f1000research.131508.2
5. Bhaskar V., Kumar S., Nair A.S., Gokul S., Krishnendu P.R., Benny S., et al. In silico development of potential InhA inhibitors through 3D-QSAR analysis, virtual screening and molecular dynamics // Journal of Biomolecular Structure and Dynamics. 2025. V. 43. N 3. P. 1329–1351. doi: 10.1080/07391102.2023.2291549
6. Moulishankar A., Sankaranarayanan M., Thirugnanasambandam S., Dhamotharan J., Mohanradja D., Sivakumar P.M. Identification of novel DNA gyrase inhibitor by combined pharmacophore modeling, QSAR analysis, molecular docking, molecular dynamics, ADMET and DFT approaches // Acta Tropica. 2024. V. 260. P. 107460. doi: 10.1016/j.actatropica.2024.107460
7. Agarwal S., Verma E., Kumar V., Lall N., Sau S., Iyer A.K., et al. An integrated computational approach of molecular dynamics simulations, receptor binding studies and pharmacophore mapping analysis in search of potent inhibitors against tuberculosis // Journal of Molecular Graphics and Modelling. 2018. V. 83. P. 17–32. doi: 10.1016/j.jmgm.2018.04.019
8. Blanes-Mira C., Fernández-Aguado P., de Andrés-López J., Fernández-Carvajal A., Ferrer-Montiel A., Fernández-Ballester G. Comprehensive survey of consensus docking for high-throughput virtual screening // Molecules. 2022. V. 28. N 1. P. 175. doi: 10.3390/molecules28010175
9. Drakontaeidi A., Pontiki E. A Review on molecular docking on HDAC isoforms: novel tool for designing selective inhibitors // Pharmaceuticals. 2023. V. 16. N 12. P. 1639. doi: 10.3390/ph16121639
10. Scior T., Bender A., Tresadern G., Medina-Franco J.L., MartinezMayorga K., et al. Recognizing pitfalls in virtual screening: a critical review // Journal of Chemical Information and Modeling. 2012. V. 52. N 4. P. 867–881. doi: 10.1021/ci200528d
11. Lavecchia A., Di Giovanni C. Virtual screening strategies in drug discovery: a critical review // Current Medicinal Chemistry. 2013. V. 20. N 23. P. 2839–2860. doi: 10.2174/09298673113209990001
12. Murugan N.A., Podobas A., Gadioli D., Vitali E., Palermo G., Markidis S. A Review on parallel virtual screening softwares for high-performance computers // Pharmaceuticals. 2022. V. 15. N 1. P. 63. doi: 10.3390/ph15010063
13. Liu J., Wang R. Classification of current scoring functions // Journal of Chemical Information and Modeling. 2015. V. 55. N 3. P. 475–482. doi: 10.1021/ci500731a
14. Balamurugan S., Krishnan A.T., Goyal D., Chandrasekaran B., Pandi B. Computation in Bioinformatics: Multidisciplinary Applications. Wiley-Scrivener, 2021. 323 p.
15. Shirali A., Stebliankin V., Karki U., Shi J., Chapagain P., Narasimhan G. A comprehensive survey of scoring functions for protein docking models // BMC Bioinformatics. 2025. V. 26. N 1. P. 25. doi: 10.1186/s12859-024-05991-4
16. Li J., Fu A., Zhang L. An Overview of scoring functions used for protein-ligand interactions in molecular docking // Interdisciplinary Sciences: Computational Life Sciences. 2019. V. 11. N 2. P. 320–328. doi: 10.1007/s12539-019-00327-w
17. Boeckler F.M., Bauer M.R., Ibrahim T.M., Vogel S.M. Use of DEKOIS 2.0 to gain insights for virtual screening // Journal of Cheminformatics. 2014. V. 6. Suppl 1. P. O24. doi: 10.1186/1758-2946-6-S1-O24
18. Cleves A.E., Jain A.N. Structure- and Ligand-Based Virtual screening on DUD-E(+): performance dependence on approximations to the binding pocket // Journal of Chemical Information and Modeling. 2020. V. 60. N 9. P. 4296–4310. doi: 10.1021/acs.jcim.0c00115
19. Guedes I.A., Pereira F.S., Dardenne L.E. Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges // Frontiers in Pharmacology. 2018. V. 9. P. 1089. doi: 10.3389/fphar.2018.01089
20. Shirali A., Stebliankin V., Karki U., Shi J., Chapagain P., Narasimhan G. A comprehensive survey of scoring functions for protein docking models // BMC Bioinformatics. 2025. V. 26. N 1. P. 25. doi: 10.1186/s12859-024-05991-4
21. de Angelo R.M., de Sousa D.S., da Silva A.P., Chiari L.P., da Silva A.B., Honorio K.M. Molecular docking: State-of-the-art scoring functions and search algorithms // Computer-Aided Drug Discovery and Design. 2025. V. 3. P. 163–198. doi: 10.1007/978-3-031-76718-0_7
22. Eldridge M.D., Murray C.W., Auton T.R., Paolini G.V., Mee R.P. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes // Journal of Computer-Aided Molecular Design. 1997. V. 11. N 5. P. 425–445. doi: 10.1023/a:1007996124545
23. Murray C.W., Auton T.R., Eldridge M.D. Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model // Journal of Computer-Aided Molecular Design. 1998. V. 12. N 5. P. 503–519. doi: 10.1023/a:1008040323669
24. Wang R., Lai L., Wang S. Further development and validation of empirical scoring functions for structure-based binding affinity prediction // Journal of Computer-Aided Molecular Design. 2002. V. 16. N 1. P. 11–26. doi: 10.1023/a:1016357811882
25. de Azevedo W.F., Dias R. Evaluation of ligand-binding affinity using polynomial empirical scoring functions // Bioorganic and Medicinal Chemistry. 2008. V. 16. N 20. P. 9378–9382. doi: 10.1016/j.bmc.2008.08.014
26. Dias R., Timmers L.F., Caceres R.A., de Azevedo W.F. Evaluation of molecular docking using polynomial empirical scoring functions // Current Drug Targets. 2008. V. 9. N 12. P. 1062–1070. doi: 10.2174/138945008786949450
27. Korb O., Stutzle T., Exner T.E. Empirical scoring functions for advanced protein-ligand docking with PLANTS // Journal of Chemical Information and Modeling. 2009. V. 49. N 1. P. 84–96. doi: 10.1021/ci800298z
28. Liu Z., Malone B., Yuan C. Empirical evaluation of scoring functions for Bayesian network model selection // BMC Bioinformatics. 2012. V. 13. Suppl 15. P. S14. doi: 10.1186/1471-2105-13-S15-S14
29. Zulhafiz N.A., Teoh T.C., Chin A.V., Chang S.W. Drug repurposing using artificial intelligence, molecular docking, and hybrid approaches: A comprehensive review in general diseases vs Alzheimer’s disease // Computer Methods and Programs in Biomedicine. 2025. V. 261. P. 108604. doi: 10.1016/j.cmpb.2025.108604
30. Fujimoto K.J., Minami S., Yanai T. Machine-learning- and knowledge-based scoring functions incorporating ligand and protein fingerprints // ACS Omega. 2022. V. 7. N 22. P. 19030–19039. doi: 10.1021/acsomega.2c02822
31. Guedes I.A., Barreto A.M.S., Marinho D., Krempser E., Kuenemann M.A., Sperandio O., et al. New machine learning and physics-based scoring functions for drug discovery // Scientific Reports. 2021. V. 11. N 1. P. 3198. doi: 10.1038/s41598-021-82410-1
32. Bitencourt-Ferreira G., Rizzotto C., de Azevedo Junior W.F. Machine learning-based scoring functions, development and applications with SAnDReS // Current Medicinal Chemistry. 2021. V. 28. N 9. P. 1746-1756. doi: 10.2174/0929867327666200515101820
33. Ghasemi F., Mehridehnavi A., Pérez-Garrido A., Pérez-Sánchez H. Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks // Drug Discovery Today. 2018. V. 23. N 10. P. 1784–1790. doi: 10.1016/j.drudis.2018.06.016
34. Matsuzaka Y., Uesawa Y. A Deep learning-based quantitative structure-activity relationship system construct prediction model of agonist and antagonist with high performance // International Journal of Molecular Sciences. 2022. V. 23. N 4. P. 2141. doi: 10.3390/ijms23042141
35. Betzi S., Suhre K., Chetrit B., Guerlesquin F., Morelli X. GFscore: a general nonlinear consensus scoring function for high-throughput docking // Journal of Chemical Information and Modeling. 2006. V. 46. N 4. P. 1704–1712. doi: 10.1021/ci0600758
36. Bar-Haim S., Aharon A., Ben-Moshe T., Marantz Y., Senderowitz H. SeleX-CS: a new consensus scoring algorithm for hit discovery and lead optimization // Journal of Chemical Information and Modeling. 2009. V. 49. N 3. P. 623–633. doi: 10.1021/ci800335j
37. Plewczynski D., Lazniewski M., von Grotthuss M., Rychlewski L., Ginalski K. VoteDock: consensus docking method for prediction of protein-ligand interactions // Journal of Computational Chemistry. 2011. V. 32. N 4. P. 568–581. doi: 10.1002/jcc.21642
38. Meli R., Morris G.M., Biggin P.C. Scoring functions for proteinligand binding affinity prediction using structure-based deep learning: a review // Frontiers in Bioinformatics. 2022. V. 2. P. 885983. doi: 10.3389/fbinf.2022.885983
39. Tran-Nguyen V.-K., Bret G., Rognan D. True accuracy of fast scoring functions to predict high-throughput screening data from docking poses: the simpler the better // Journal of Chemical Information and Modeling. 2021. V. 61. N 6. P. 2788–2797. doi: 10.1021/acs.jcim.1c00292
40. Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S., et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility // Journal of Computational Chemistry. 2009. V. 30. N 16. P. 2785–2791. doi: 10.1002/jcc.21256
41. Bitencourt-Ferreira G., Pintro V.O., de Azevedo W.F. Docking with AutoDock4 // Methods in Molecular Biology. 2019. V. 2053. P. 125-148. doi: 10.1007/978-1-4939-9752-7_9
42. Santos-Martins D., Solis-Vasquez L., Tillack A.F., Sanner M.F., Koch A., Forli S. Accelerating AutoDock4 with GPUs and gradientbased local search // Journal of Chemical Theory and Computation. 2021. V. 17. N 2. P. 1060–1073. doi: 10.1021/acs.jctc.0c01006
43. Obiol-Pardo C., Rubio-Martinez J. Comparative evaluation of MMPBSA and XSCORE to compute binding free energy in XIAPpeptide complexes // Journal of Chemical Information and Modeling. 2007. V. 47. N 1. P. 134–142. doi: 10.1021/ci600412z
44. Pencheva T., Soumana O.S., Pajeva I., Miteva M.A. Post-docking virtual screening of diverse binding pockets: comparative study using DOCK, AMMOS, X-Score and FRED scoring functions // European Journal of Medicinal Chemistry. 2010. V. 45. N 6. P. 2622–2628. doi: 10.1016/j.ejmech.2009.12.025
45. Neudert G., Klebe G. DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes // Journal of Chemical Information and Modeling. 2011. V. 51. N 10. P. 2731–2745. doi: 10.1021/ci200274q
46. Wang W.J., Huang Q., Zou J., Li L.L., Yang S.Y. TS-Chemscore, a target-specific scoring function, significantly improves the performance of scoring in virtual screening // Chemical Biology and Drug Design. 2015. V. 86. N 1. P. 1–8. doi: 10.1111/cbdd.12470
47. Eberhardt J., Santos-Martins D., Tillack A.F., Forli S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings // Journal of Chemical Information and Modeling. 2021. V. 61. N 8. P. 3891–3898. doi: 10.1021/acs.jcim.1c00203
48. Lu H., Wei Z., Wang C., Guo J., Zhou Y., Wang Z., et al. Redesigning Vina@QNLM for ultra-large-scale molecular docking and screening on a sunway supercomputer // Frontiers in Chemistry. 2021. V. 9. P. 750325. doi: 10.3389/fchem.2021.750325
Рецензия
Для цитирования:
Спринджук М.В., Кукарцев В.В., Ноздрачева А.В., Кончиц А.П., Владыко А.С., Берник В.И., Сукач Е.И. Оценочные функции моделей молекулярного докинга белков в автоматизированных системах обработки и анализа биоинформационных данных. Научно-технический вестник информационных технологий, механики и оптики. 2026;26(4):683-694. https://doi.org/10.17586/2226-1494-2026-26-4-683-694
For citation:
Sprindzuk M.V., Kukartsev V.V., Nozdracheva A.V., Konchits A.P., Vladyko A.S., Bernik V.I., Sukach E.I. Scoring functions of protein molecular docking models in automated systems for processing and analyzing bioinformatics data. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2026;26(4):683-694. (In Russ.) https://doi.org/10.17586/2226-1494-2026-26-4-683-694
JATS XML






























