Predicting biomolecular binding kinetics: A review

TL;DR

This review discusses recent computational methods for predicting biomolecular binding kinetics, emphasizing their importance in drug design and highlighting advances in models like machine learning and molecular simulations.

Contribution

It provides a comprehensive overview of recent computational approaches for modeling biomolecular binding kinetics and suggests directions for future research.

Findings

Machine learning enhances prediction accuracy of binding rates.

Molecular Dynamics simulations reveal detailed dissociation mechanisms.

Quantitative models correlate residence time with drug efficacy.

Abstract

Biomolecular binding kinetics including the association (kon) and dissociation (koff) rates are critical parameters for therapeutic design of small-molecule drugs, peptides and antibodies. Notably, drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling and Machine Learning have been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.