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Research PaperResearchia:202610.15041

Memory-resolved prediction of ligand residence times on the minutes timescale

Hossein Batebi

Abstract

AI-enabled computational drug discovery is advancing rapidly in predicting molecular structures, binding poses and affinities, but ligand dissociation kinetics remain harder to access. Bound-state residence time, important for biological response and kinetic selectivity, is not fixed by equilibrium affinity but depends on the dissociation free-energy landscape and dissipative friction effects. Here, we show that restrained umbrella-sampling trajectories can be used to calculate residence times o...

Submitted: October 15, 2026Subjects: AI; AI in Drug Discovery

Description / Details

AI-enabled computational drug discovery is advancing rapidly in predicting molecular structures, binding poses and affinities, but ligand dissociation kinetics remain harder to access. Bound-state residence time, important for biological response and kinetic selectivity, is not fixed by equilibrium affinity but depends on the dissociation free-energy landscape and dissipative friction effects. Here, we show that restrained umbrella-sampling trajectories can be used to calculate residence times on the minutes timescale. Time-resolved coordinate fluctuations yield friction profiles that incorporate non-Markovian effects on dissociation kinetics. Combining them with the free-energy profile, we predict a residence time of Ο„resΟ„_{\rm res} = 275 s for the adenosine A2A_{2A} receptor with ZM241385 (comparable to the experimental value of 125 s) and reproduce the experimental ranking of wild type and two receptor mutants. For HSP90 with compound 18 and RNase S with the S15 peptide, we predict Ο„resΟ„_{\rm res} = 2,169 s and 2,259 s (experimental values are 833 s and 2,439 s). Neglecting memory effects on residence kinetics overestimates Ο„resΟ„_{\rm res} by factors of 2.6 to 29.


Source: arXiv:2610.20271v1 - http://arxiv.org/abs/2610.20271v1 PDF: https://arxiv.org/pdf/2610.20271v1 Original Link: http://arxiv.org/abs/2610.20271v1

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Date:
Oct 15, 2026
Topic:
AI in Drug Discovery
Area:
AI
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