# Predicting biomolecular binding kinetics: A review

Source: [Predicting biomolecular binding kinetics: A review](<https://www.sota2.com/research/paper/68fa5f3a1e209131ce9e8663>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:11:53.560Z

Authors: Jinan Wang, Hung N. Do, Kushal Koirala, Yinglong Miao

Published: 2022-11-05T12:19:36.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.02891>)

[PDF](<https://arxiv.org/pdf/2211.02891.pdf>)