AI Article Synopsis
- Biomolecular binding kinetics, which involve the rates of association (binding) and dissociation (unbinding), are essential for designing effective small-molecule drugs, peptides, and antibodies.
- Research indicates that the time a drug molecule remains bound (residence time) is a better predictor of its effectiveness than just the strength of the binding (affinity).
- Various modeling techniques, such as quantitative structure-kinetic relationship models, Molecular Dynamics simulations, and Machine Learning, have been developed to study and predict these binding and dissociation processes; this review highlights recent advances and future directions in the field.
Article Abstract
Biomolecular binding kinetics including the association () and dissociation () rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the 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 has 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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| http://dx.doi.org/10.1021/acs.jctc.2c01085 | DOI Listing |
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