Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective
Katya Ahmad, Andrea Rizzi, Riccardo Capelli, Davide Mandelli, Wenping Lyu, Paolo Carloni
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.
Topics & Concepts
Sampling (signal processing)Perspective (graphical)Receptor–ligand kineticsCurrent (fluid)KineticsStatistical physicsEconometricsEstimationChemistryBiological systemComputer scienceStatisticsComputational biologyBiologyMathematicsPhysicsThermodynamicsArtificial intelligenceEconomicsManagementFilter (signal processing)Computer visionQuantum mechanicsComputational Drug Discovery MethodsProtein Structure and DynamicsMachine Learning in Materials Science