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Cited by 2Computational Drug Discovery MethodsProtein Structure and Dynamicsvaccines and immunoinformatics approaches

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De novo protein–ligand design including protein flexibility and conformational adaptation

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Agamia, Jakob, Zacharias, Martin·2026·Bioinformatics
DOI 10.1093/bioinformatics/btag027PMC PMC12944823

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Abstract

Motivation: The rational design of chemical compounds that bind to a desired protein target molecule is a major goal of drug discovery. Most current molecular docking but also fragment-based buildup or machine learning-based generative drug design approaches employ a rigid protein target structure. Results: Based on recent progress in predicting protein structures and complexes with chemical compounds, we have designed an approach, AI-MCLig, to optimize a chemical compound bound to a fully flexible and conformationally adaptable protein binding region. During a Monte Carlo (MC)-type simulation to randomly change a chemical compound, the target protein–compound complex is completely rebuilt at every MC step using the Chai-1 protein structure prediction program. Besides compound flexibility it allows the protein to adapt to the chemically changing compound. MC protocols based on atom-/bond-type changes or based on combining larger chemical fragments have been tested. Simulations on four test targets resulted in potential ligands that show very good binding scores comparable to experimentally known binders using several different scoring schemes. The MC-based compound design approach is complementary to existing approaches and could help for the rapid design of putative binders including induced fit of the protein target. Availability and implementation: Datasets, examples, and source code are available on our public GitHub repository https://github.com/JakobAgamia/AI-MCLig and on Zenodo at https://doi.org/10.5281/zenodo.17800140.

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Research works printed but not linked: 40

  1. Beier, 2010, Tackling the challenges posed by target flexibility in drug design, Expert Opin Drug Discov, 5, 347, 10.1517/17460441003713462 · DOI
  2. Bickerton, 2012, Quantifying the chemical beauty of drugs, Nat Chem, 4, 90, 10.1038/nchem.1243 · DOI
  3. Carhart, 1985, Atom pairs as molecular features in structure-activity studies: definition and applications, J Chem Inf Comput Sci, 25, 64, 10.1021/ci00046a002 · DOI
  4. Case, 2023, Ambertools, J Chem Inf Model, 63, 6183, 10.1021/acs.jcim.3c01153 · DOI
  5. Case, 2025, Amber 2025
  6. Chai Discovery team, 2024, 10.1101/2024.10.10.615955, · DOI · Paper page
  7. Cihan Sorkun, 2022, Chemplot, a python library for chemical space visualization, Chem Methods, 2, e202200005, 10.1002/cmtd.202200005 · DOI
  8. Corrêa Veríssimo, 2025, Ultra-large virtual screening: definition, recent advances, and challenges in drug design, Mol Inform, 44, e202400305, 10.1002/minf.202400305 · DOI
  9. Degen, 2008, On the art of compiling and using ‘drug-like’ chemical fragment spaces, ChemMedChem, 3, 1503, 10.1002/cmdc.200800178 · DOI
  10. Delaney, 2004, ESOL: estimating aqueous solubility directly from molecular structure, J Chem Inf Comput Sci, 44, 1000, 10.1021/ci034243x · DOI
  11. Eberhardt, 2021, Autodock vina 1.2.0: new docking methods, expanded force field, and python bindings, J Chem Inf Model, 61, 3891, 10.1021/acs.jcim.1c00203 · DOI
  12. Ertl, 2009, Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions, J Cheminform, 1, 8, 10.1186/1758-2946-1-8 · DOI · Paper page
  13. Goetz, 2012, Routine microsecond molecular dynamics simulations with AMBER—part I: generalized born, J Chem Theo Comput, 8, 1542, 10.1021/ct200909j · DOI
  14. Grygorenko, 2020, Generating multibillion chemical space of readily accessible screening compounds, iScience, 23, 101873, 10.1016/j.isci.2020.101873 · DOI
  15. Jorgensen, 1983, Comparison of simple potential functions for simulating liquid water, J Chem Phys, 79, 926, 10.1063/1.445869 · DOI
  16. Laskowski, 2011, Ligplot+: multiple ligand–protein interaction diagrams for drug discovery, J Chem Inf Model, 51, 2778, 10.1021/ci200227u · DOI
  17. Le Grand, 2013, SPFP: speed without compromise - a mixed precision model for GPU accelerated molecular dynamics simulations, Comp Phys Comm, 184, 374, 10.1016/j.cpc.2012.09.022 · DOI
  18. Liu, 2010, Fast determination of the optimal rotational matrix for macromolecular superpositions, J Comput Chem, 31, 1561, 10.1002/jcc.21439 · DOI
  19. Liu, 2021, Computational approaches for de novo drug design: past, present, and future, Methods Mol Biol, 2190, 139, 10.1007/978-1-0716-0826-5_6 · DOI
  20. Healy J, McInnes L., 2024, Nat Rev Methods Primers, 10.1038/s43586-024-00363-x · DOI
  21. Martino, 2021, Mapping, structure and modulation of PPI, Front Chem, 9, 718405, 10.3389/fchem.2021.718405 · DOI
  22. Michaud-Agrawal, 2011, Mdanalysis: a toolkit for the analysis of molecular dynamics simulations, J Comput Chem, 32, 2319, 10.1002/jcc.21787 · DOI
  23. Miller, 2012, Mmpbsa.py: an efficient program for end-state free energy calculations, J Chem Theory Comput, 8, 3314, 10.1021/ct300418h · DOI
  24. Mouchlis, 2021, Advances in de novo drug design: from conventional to machine learning methods, Int J Mol Sci, 22, 1676, 10.3390/ijms22041676 · DOI
  25. Salomon-Ferrer, 2013, Routine microsecond molecular dynamics simulations with AMBER—part II: particle mesh Ewald, J Chem Theory Comput, 9, 3878, 10.1021/ct400314y · DOI
  26. Schneider, 2020, Rethinking drug design in the artificial intelligence era, Nat Rev Drug Discov, 19, 353, 10.1038/s41573-019-0050-3 · DOI · Paper page
  27. Sindt, 2025, On the difficulty to rescore hits from ultralarge docking screens, J Chem Inf Model, 65, 5553, 10.1021/acs.jcim.5c00730 · DOI
  28. Śledź, 2018, Protein structure-based drug design: from docking to molecular dynamics, Curr Opin Struct Biol, 48, 93, 10.1016/j.sbi.2017.10.010 · DOI
  29. Tang, 2024, A survey of generative ai for de novo drug design: new frontiers in molecule and protein generation, Brief Bioinfo, 25, bbae338, 10.1093/bib/bbae338 · DOI
  30. Tang, 2024, Recent advances in automated structure-based de novo drug design, J Chem Inf Model, 64, 1794, 10.1021/acs.jcim.4c00247 · DOI
  31. Theobald, 2005, Rapid calculation of RMSDs using a quaternion-based characteristic polynomial, Acta Crystallogr A, 61, 478, 10.1107/S0108767305015266 · DOI
  32. Trott, 2010, Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J Comput Chem, 31, 455, 10.1002/jcc.21334 · DOI · Paper page
  33. Van Der Walt, 2011, The numpy array: a structure for efficient numerical computation, Comput Sci Eng, 13, 22, 10.1109/MCSE.2011.37 · DOI · Paper page
  34. Wang, 2006, Automatic atom type and bond type perception in molecular mechanical calculations, J Mol Graph Model, 25, 247, 10.1016/j.jmgm.2005.12.005 · DOI
  35. Wang, 2006, Development and testing of a general amber force field, J Comput Chem, 25, 1157, 10.1002/jcc.20035 · DOI
  36. Wang, 2022, Deep learning approaches for de novo drug design: an overview, Curr Opin Struct Biol, 72, 135, 10.1016/j.sbi.2021.10.001 · DOI
  37. Weininger, 1988, Smiles, a chemical language and information system. 1. Introduction to methodology and encoding rules, J Chem Inf Comput Sci, 28, 31, 10.1021/ci00057a005 · DOI · Paper page
  38. Xie, 2022, Advances and challenges in de novo drug design using three-dimensional deep generative models, J Chem Inf Model, 62, 2269, 10.1021/acs.jcim.2c00042 · DOI
  39. Zahiri, 2020, Protein complex prediction: a survey, Genomics, 112, 174, 10.1016/j.ygeno.2019.01.011 · DOI
  40. Zdrazil, 2023, The ChEMBL database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods, Nucl Acids Res, 52, 1180, 10.1093/nar/gkad1004 · DOI

Metadata

Authors
Agamia, J., & Zacharias, M.
Affiliations
Technical University of Munich (DE)
Corresponding author
Zacharias, Martin
ORCID
Zacharias, Martin
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2026-01-22
Volume
42
Issue
2
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
DOI
10.1093/bioinformatics/btag027
PMC
PMC12944823
OpenAlex
W7125487729
Cited by
2
References
4
Citations by year
2026: 2
FWCI
7.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
Funders
  • Deutsche Forschungsgemeinschaft (DFG Za153/29-1)
  • Erlangen National High Performance Center (b118bb)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
de novo ligand design, protein-ligand binding, protein flexibility, conformational adaptation, induced fit, Monte Carlo simulation, protein structure prediction, drug discovery
MeSH terms
Binding Sites, Ligands, Monte Carlo Method, Protein Binding, Protein Conformation, Proteins, Drug Design, Molecular Docking Simulation

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