Baseline model for predicting protein-ligand unbinding kinetics through machine learning

Nurlybek Amangeldiuly, Dmitry Karlov, Maxim V. Fedorov

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Derivation of structure–kinetics relationships can help rational design and development of new small-molecule drug candidates with desired residence times. Efforts are now being directed toward the development of efficient computational methods. Currently, there is a lack of solid, high-throughput binding kinetics prediction approaches on bigger datasets. We present a prediction method for binding kinetics based on the machine learning analysis of protein–ligand structural features, which can serve as a baseline for more sophisticated methods utilizing molecular dynamics (MD). We showed that the random forest algorithm is capable of learning the protein binding site secondary structure and backbone/side-chain features to predict the binding kinetics of protein–ligand complexes but still with inferior performance to that of MD-based descriptor analysis. MD simulations had been applied to a limited number of targets and a series of ligands in terms of kinetics analysis, and we believe that the developed approach may guide new studies. The method was trained on a newly curated database of 501 protein–ligand unbinding rate constants, which can also be used for testing and training the binding kinetics prediction models.
Original languageEnglish
Pages (from-to)5946-5956
Number of pages11
JournalJournal of Chemical Information and Modeling
Issue number12
Early online date13 Nov 2020
Publication statusPublished - 28 Dec 2020


  • kinetics
  • ligands
  • machine learning
  • molecular dynamics simulation
  • protein binding
  • proteins


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