pocketz.doi.bio/2026-03-31_155435_linkspringercom_s11030-026-11521-6
Integrating modern computational techniques elevates binding mode identification for molecular fragments | Molecular Diversity | Springer Nature Link
URL: https://link.springer.com/article/10.1007/s11030-026-11521-6
Saved: 2026-03-31T22:54:35.472Z
Sergei Evteev, Fedor Sizov, Anna Pastukhova, Alexey Ereshchenko, Denis Adjugim & Yan Ivanenkov
113 Accesses
1 Altmetric
Explore all metrics
Abstract
Molecular docking is a routine and essential procedure in computational drug discovery. However, it is well known that current techniques are still far from perfect, especially when being applied to molecular fragments. In this work, we combined advantages of traditional energy-based docking, a diffusion model, intermolecular interaction filters, and ML-assisted scoring function to achieve higher accuracy. We compiled a comprehensive dataset of molecular fragments to evaluate our new protocol - HybriDock - in comparison with three other docking algorithms including DiffDock, Matcha and AutoDock Vina. HybriDock demonstrated remarkable improvements over these methods, highlighting the advantage of combining modern molecular modeling approaches. Additionally, we analyzed the influence of fragment chemical properties and protein family on the ability to predict accurate binding modes and highlighted up-to-date limitations and future directions.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Explore related subjects Discover the latest articles, books and news in related subjects, suggested using machine learning. Chromosome conformation capture-based methods Computational Chemistry Molecular Modelling Molecular Biophysics Molecular Dynamics Protein-Ligand Interactions Computational Drug Discovery and Design Strategies Data availability
All experiments were performed with use of open source databases and software. All additional details can be requested via email.
References
Abramson J, Adler J, Dunger J et al (2024) Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630(8016):493–500. https://doi.org/10.1038/s41586-024-07487-w
Article CAS PubMed PubMed Central Google Scholar
Bender BJ, Gahbauer S, Luttens A et al (2021) A practical guide to large-scale docking. Nat Protoc 16(10):4799–4832. https://doi.org/10.1038/s41596-021-00597-z
Article CAS PubMed PubMed Central Google Scholar
Buttenschoen M, Morris GM, Deane CM (2023) PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chem Sci 15(9):3130–3139. https://doi.org/10.1039/d3sc04185a
Article CAS PubMed PubMed Central Google Scholar
Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T (2022) Diffdock: diffusion steps, twists, and turns for molecular docking. arXiv:2510.14586
Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J Chem Inf Model 61(8):3891–3898. https://doi.org/10.1021/acs.jcim.1c00203
Article CAS PubMed PubMed Central Google Scholar
Evteev SA, Ereshchenko AV, Ivanenkov YA (2023) SiteRadar: utilizing graph machine learning for precise mapping of protein-ligand-binding sites. J Chem Inf Model 63(4):1124–1132. https://doi.org/10.1021/acs.jcim.2c01413
Article CAS PubMed Google Scholar
Evteev S, Ivanenkov Y, Semenov I et al (2024) Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study. Front Chem 12:1382512. https://doi.org/10.3389/fchem.2024.1382512
Article CAS PubMed PubMed Central Google Scholar
Evteev S, Ereshchenko A, Adjugim D et al (2025) Skittles: GNN-assisted pseudo-ligands generation and its application for binding sites classification and affinity prediction. Proteins. https://doi.org/10.1002/prot.70056
Article PubMed Google Scholar
Frolova D, Daulbaev T, Sevriugov E, et al. (2025) Matcha: multi-stage riemannian flow matching for accurate and physically valid molecular docking. arXiv:2510.14586
Herron L, Dakka J, Yao K, Shi D, Zhang Y, Jerome SV (2025) DiffDock-Glide: a hybrid physics-based and data-driven approach to molecular docking bioRxiv 2025.06.02.657461
Hu X, Shelver WH (2003) Docking studies of matrix metalloproteinase inhibitors: zinc parameter optimization to improve the binding free energy prediction. J Mol Graph Model 22(2):115–126. https://doi.org/10.1016/S1093-3263(03)00153-0
Article CAS PubMed Google Scholar
Jiang D, Hsieh CY, Wu Z et al (2021) InteractionGraphNet: a novel and efficient deep graph representation learning framework for accurate protein-ligand interaction predictions. J Med Chem 64(24):18209–18232. https://doi.org/10.1021/acs.jmedchem.1c01830
Article CAS PubMed Google Scholar
Khan O, Jones G, Kozakov D, Beglov D, Joseph-McCarthy D, Vajda S (2025) E-FTMap: a protein structure based pharmacophore identification server for guiding fragment expansion. J Mol Biol 437(15):168956. https://doi.org/10.1016/j.jmb.2025.168956
Article CAS PubMed PubMed Central Google Scholar
Krishna R, Wang J, Ahern W et al (2024) Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384(6693):eadl2528. https://doi.org/10.1126/science.adl2528
Article CAS PubMed Google Scholar
Leidner F, Kurt Yilmaz N, Paulsen J, Muller YA, Schiffer CA (2018) Hydration structure and dynamics of inhibitor-bound HIV-1 protease. J Chem Theory Comput 14(5):2784–2796. https://doi.org/10.1021/acs.jctc.8b00097
Article CAS PubMed PubMed Central Google Scholar
Lexa KW, Carlson HA (2012) Protein flexibility in docking and surface mapping. Q Rev Biophys 45(3):301–343. https://doi.org/10.1017/S0033583512000066
Article CAS PubMed PubMed Central Google Scholar
Manglik A, Kim TH, Masureel M et al (2015) Structural insights into the dynamic process of β2-Adrenergic Receptor signaling. Cell 161(5):1101–1111. https://doi.org/10.1016/j.cell.2015.04.043
Article CAS PubMed PubMed Central Google Scholar
Mqawass G, Popov P (2024) graphLambda: fusion graph neural networks for binding affinity prediction. J Chem Inf Model 64(7):2323–2330. https://doi.org/10.1021/acs.jcim.3c00771
Article CAS PubMed Google Scholar
Neves MA, Totrov M, Abagyan R (2012) Docking and scoring with ICM: the benchmarking results and strategies for improvement. J Comput Aided Mol Des 26(6):675–686. https://doi.org/10.1007/s10822-012-9547-0
Article CAS PubMed PubMed Central Google Scholar
Pettersen EF, Goddard TD, Huang CC et al (2021) UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci 30(1):70–82. https://doi.org/10.1002/pro.3943
Article CAS PubMed Google Scholar
Stroganov OV, Novikov FN, Stroylov VS, Kulkov V, Chilov GG (2008) Lead finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening. J Chem Inf Model 48(12):2371–2385. https://doi.org/10.1021/ci800166p
Article CAS PubMed Google Scholar
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52(4):609–623. https://doi.org/10.1002/prot.10465
Article CAS PubMed Google Scholar
wwPDB consortium (2019) Protein Data Bank: the single global archie for 3D macromolecular structure data. Nucleic Acids Res 47(D1):D520–D528. https://doi.org/10.1093/nar/gky949
Article CAS Google Scholar
Zdrazil B, Felix E, Hunter F et al (2024) The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucl Acids Res 52(D1):D1180–D1192. https://doi.org/10.1093/nar/gkad1004
Article CAS PubMed PubMed Central Google Scholar
Download references
Author information Authors and Affiliations
Dukhov Automatics Research Institute (VNIIA), Moscow, Russian Federation
Sergei Evteev, Fedor Sizov, Anna Pastukhova, Alexey Ereshchenko, Denis Adjugim & Yan Ivanenkov
Contributions
S.E.—investigation, writing original manuscript; F.S.—code writing, investigation, manuscript editing; A.P.—code writing, investigation, manuscript editing; A.E.—code writing, manuscript editing; D.A.—code writing, manuscript editing; Ya.I.—supervision, manuscript editing.
Corresponding author
Correspondence to Sergei Evteev.
Ethics declarations Conflict of interest
The authors declare that they have no conflict of interest.
Additional information Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary file1 (DOCX 264 kb) (download DOCX ) Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
About this article Cite this article
Evteev, S., Sizov, F., Pastukhova, A. et al. Integrating modern computational techniques elevates binding mode identification for molecular fragments. Mol Divers (2026). https://doi.org/10.1007/s11030-026-11521-6
Download citation
Received 05 December 2025
Accepted 13 March 2026
Published 25 March 2026
Version of record 25 March 2026
DOI https://doi.org/10.1007/s11030-026-11521-6
Keywords Molecular docking Molecular fragments Diffusion model Intermolecular interactions Machine learning