Recent advances in computational methods for studying ligand binding kinetics

TL;DR

This review discusses recent computational techniques, including enhanced sampling and machine learning, for predicting ligand binding kinetics and understanding mechanisms in drug discovery, focusing on efficiency and accuracy improvements.

Contribution

It provides a comprehensive comparison of new computational methods applied to key protein-drug systems, highlighting approaches that balance prediction accuracy and computational efficiency.

Findings

Enhanced sampling and machine learning methods improve kinetic rate predictions.

Strategies to reduce computational errors are discussed.

Methods reveal factors influencing residence times, selectivity, and resistance.

Abstract

Binding kinetic parameters can be correlated with drug efficacy, which led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms in recent years. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.