Predicting biomolecular binding kinetics: A review

2022/11/05 by Jinan Wang, Hung N. Do, Wang, Jinan +5 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Biomolecules (q-bio.BM) #Computational Drug Discovery Methods #FOS: Biological sciences #Monoclonal and Polyclonal Antibodies Research #Protein purification and stability

paper · pdf · doi:10.48550/arxiv.2211.02891

openalex publication_date 2022/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

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.

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