Scoring functions of molecular protein docking models: classification and application in bioinformatics

This review article presents a generalized, integrated classification of scoring functions for molecular docking of protein interaction models. It describes various types of scoring functions and reports relevant issues in biopharmaceuticals, medical cybernetics, computational biology and biophysics, applied mathematics. This article may be of interest to a wide range of readers, including scientists from various fields, software engineers, developing automated bioinformation processing systems, lecturers on relevant topics, graduate and postgraduate students in bioinformatics, systems analysis, medical software systems research

1. Ugbaja S.C., Mushebenge A.G-A., 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;18(3):419. https://doi.org/10.3390/ph18030419.

2. Tanoli Z., Fernández-Torras A., Özcan 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;24(7):521–542. https://doi.org/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;26(3):93–101. https://doi.org/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 SARS-CoV-2 S-glycoprotein and the ACE2 receptor. F1000Research. 2024;12:1452. https://doi.org/10.12688/f1000research.131508.2.

5. Bhaskar V., Kumar S., Sujathan Nair A., Gokul S., Rajappan Krishnendu P., Benny S., et al. In silico development of potential InhA inhibitors through 3D-QSAR analysis, virtual screening and molecular dynamics. Journal of Biomolecular Structure & Dynamics. 2025;43(3):1329–1351. https://doi.org/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;260:107460. https://doi.org/10.1016/j.actatropica.2024.107460.

7. Agarwal S., Verma E., Kumar V., Lall N., Sau S. Iyer Arun 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;83:17–32. https://doi.org/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. 2023;28(1):175. https://doi.org/10.3390/molecules28010175.

9. Drakontaeidi A., Pontiki E.A. Review on molecular docking on HDAC isoforms: Novel tool for designing selective inhibitors. Pharmaceuticals. 2023;16(12):1639. https://doi.org/10.3390/ph16121639.

10. Scior T., Bender A., Tresadern G., Medina-Franco J.L., Martinez-Mayorga K., Langer T. Recognizing pitfalls in virtual screening: A critical review. Journal of Chemical Information and Modeling. 2012;52(4):867-881. https://doi.org/10.1021/ci200528d.

11. Lavecchia A., Di Giovanni C. Virtual screening strategies in drug discovery: A critical review. Current Medicinal Chemistry.2013;20(23):2839–2860. https://doi.org/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;15(1). https://doi.org/10.3390/ph15010063.

13. Shirali A., Stebliankin V., Shi J., Narasimhan G. A comprehensive survey of scoring functions for protein docking models. bioRxiv. 2023.12.29.573663. https://doi.org/10.1101/2023.12.29.573663.

14. 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;9:1089. https://doi.org/10.3389/fphar.2018.01089.

15. Liu, J., Wang, R. Classification of current scoring functions. Journal of Chemical Information and Modeling. 2015;55(3):475–482. https://doi.org/10.1021/ci500731a.

16. Balamurugan S., Krishnan A.T., Goyal D., Chandrasekaran B., Pandi B. Computation in Bioinformatics: Multidisciplinary Applications. John Wiley & Sons; 2021.

17. 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;26:25. https://doi.org/10.1186/s12859-024-05991-4.

18. 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;11(2):320–328. https://doi.org/10.1007/s12539-019-00327-w.

19. 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;6(Suppl 1):O24. https://doi.org/10.1186/1758-2946-6-S1-O24.

20. Cleves A.E., Jain A.N. Structureand ligand-based virtual screening on DUD-E(+): performance dependence on approximations to the binding pocket. Journal of Chemical Information and Modeling. 2020;60(9):4296–4310. https://doi.org/10.1021/acs.jcim.0c00115.

21. 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;11(5):425-445. https://doi.org/10.1023/A:1007996124545.

22. Murray C.W., Auton T.R., Eldridge M.D. Empirical scoring functions. II. The testing of an empirical scoring function for the predictionofligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model. Journal of Computer-Aided Molecular Design. 1998;12(5):503–519. https://doi.org/10.1023/A:1008040323669.

23. 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;16(1):11–26. https://doi.org/10.1023/A:1016357811882.

24. de Azevedo Jr. W.F., Dias R. Evaluation of ligand-binding affinity using polynomial empirical scoring functions. Bioorganic & Medicinal Chemistry. 2008;16(20):9378-9382. https://doi.org/10.1016/j.bmc.2008.08.014.

25. Dias R., Timmers L.F., Caceres R.A., de Azevedo Jr. W.F. Evaluation of molecular docking using polynomial empiricalscoringfunctions. Current Drug Targets. 2008;9(12):1062–1070. https://doi.org/10.2174/138945008786949450.

26. Korb O., Stutzle T., Exner T.E. Empirical scoring functions for advanced protein-ligand docking with PLANTS. Journal of Chemical Information and Modeling. 2009;49(1):84–96. https://doi.org/10.1021/ci800298z.

27. Liu Z., Malone B., Yuan C. Empirical evaluation of scoring functions for Bayesian network model selection. BMC Bioinformatics. 2012;13(Suppl 15):S14. https://doi.org/10.1186/1471-2105-13-S15-S14.

28. 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. Compuert Methods Programs Biomedicine. 2025;261:108604. https://doi.org/10.1016/j.cmpb.2025.108604.

29. Fujimoto K.J., Minami S., Yanai T. Machine-learningand knowledge-based scoring functions incorporating ligand and protein fingerprints. ACS Omega. 2022;7(22):19030–19039. https://doi.org/10.1021/acsomega.2c02822.

30. 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;11(1):3198. https://doi.org/10.1038/s41598-021-82410-1.

31. Bitencourt-Ferreira G., Rizzotto C., de Azevedo Junior W.F. Machine learning-based scoring functions, development and applications with SAnDReS. Current Medicinal Chemistry. 2021;28(9):1746–1756. https://doi.org/10.2174/0929867327666200515101820.

32. 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;46(4):1704–1712. https://doi.org/10.1021/ci0600758.

33. 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. 2009;49(3):623–633. https://doi.org/10.1021/ci800335j.

34. 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;32(4):568–581. https://doi.org/10.1002/jcc.21642.

35. Meli R., Morris G.M., Biggin P.C. Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: A review. Frontiers in Bioinformatics. 2022;2:885983. https://doi.org/10.3389/fbinf.2022.885983.

36. 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;61(6):2788–2797. https://doi.org/10.1021/acs.jcim.1c00292.

37. 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;30(16):2785–2791. https://doi.org/10.1002/jcc.21256.

38. Bitencourt-Ferreira G., Pintro V.O., de Azevedo Jr. W.F. Docking with AutoDock4. In: de Azevedo Jr. W. (eds) Docking Screens for Drug Discovery. Methods in Molecular Biology, Volume 2053. Humana, New York; 2019. pp. 125–148. https://doi.org/10.1007/978-1-4939-9752-7_9.

39. Santos-Martins D., Solis-Vasquez L., Tillack A.F., Sanner M.F., Koch A., Forli S. Accelerating AutoDock4 with GPUs and gradient-based local search. Journal of Chemical Theory and Computation. 2021;17(2):1060– 1073. https://doi.org/10.1021/acs.jctc.0c01006.

40. Obiol-Pardo C., Rubio-Martinez J. Comparative evaluation of MMPBSA and XSCORE to compute binding free energy in XIAP-peptide complexes. Journal of Chemical Information and Modeling. 2007;47(1):134–142. https://doi.org/10.1021/ci600412z.

41. 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;45(6):2622–2628. https://doi.org/10.1016/j.ejmech.2009.12.025.

42. Neudert G., Klebe G. DSX: A knowledge-based scoring function for the assessment of protein-ligand complexes. Journal of Chemical Information and Modeling. 2011;51(10):2731–2745. https://doi.org/10.1021/ci200274q.

43. 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 & Drug Design. 2015;86(1):1–8. https://doi.org/10.1111/cbdd.12470.

44. 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;61(8):3891–3898. https://doi.org/10.1021/acs.jcim.1c00203.

45. 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;9:750325. https://doi.org/10.3389/fchem.2021.750325.