The dissociation rate (k(off)) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k(off). Next, we discuss the impact of the potential energy function models on the accuracy of calculated k(off) values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective / K. Ahmad, A. Rizzi, R. Capelli, D. Mandelli, W. Lyu, P. Carloni. - In: FRONTIERS IN MOLECULAR BIOSCIENCES. - ISSN 2296-889X. - 9:(2022 Jun 08), pp. 899805.1-899805.17. [10.3389/fmolb.2022.899805]

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

R. Capelli;
2022

Abstract

The dissociation rate (k(off)) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k(off). Next, we discuss the impact of the potential energy function models on the accuracy of calculated k(off) values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.
QM/MM; drug discovery; enhanced sampling; kinetics; machine learning; molecular dynamics; parallel computing
Settore FIS/07 - Fisica Applicata(Beni Culturali, Ambientali, Biol.e Medicin)
Settore BIO/10 - Biochimica
Settore CHIM/02 - Chimica Fisica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/933797
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