Baseline Model for Predicting Protein–Ligand Unbinding Kinetics through Machine Learning

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.