Editorial: Mechanisms, thermodynamics and kinetics of ligand binding revealed from molecular simulations and machine learning

Abstract

Editorial on the Research Topic Mechanisms, thermodynamics and kinetics of ligand binding revealed from molecular simulations and machine learning Ligand binding plays an essential role in cellular signaling. Detailed understanding of the mechanisms, structures, thermodynamics and kinetics of ligand binding is central to drug discovery in the pharmaceutical industry and academia (Baron and McCammon, 2013;Peng et al., 2019). Despite this critical importance, such tasks remain challenging in computational chemistry and biophysics. Molecular docking has proven useful in rapid virtual screening of small molecules for drug discovery, although it is often difficult to fully incorporate receptor flexibility into the docking calculations. Recent developments in computing hardware and simulation algorithms have enabled molecular dynamics (MD) simulations to capture dynamic ligand binding and dissociation processes. These simulations can then be analyzed to compute both thermodynamic free energies and kinetic rates of ligand binding (Pang and Zhou, 2017;Tang et al., 2017;Nunes-Alves et al., 2020;Wang et al., 2022). In addition, Brownian dynamics simulations have been very efficient in generating a large number of ligand binding trajectories and estimating the binding kinetic rates (Huber and McCammon, 2019;Muñiz-Chicharro et al., 2022). Finally, emerging machine learning techniques have greatly enhanced molecular simulations and facilitated analysis of the simulation trajectories (Glielmo et al., 2021).This Research Topic is focused on studies of the pathways, mechanisms, free energies and kinetics of ligand binding to target receptors. We encouraged both method development and application papers. Potential techniques used to address these problems include molecular docking, MD, Brownian dynamics, and machine learning approaches. Systems of interest broadly involve ligand binding to any type of receptors, including proteins, nucleic acids, materials, and so on.Carloni et al. have reviewed recent major advancements in molecular simulation methodologies for predicting dissociation rate (k off ), a parameter of fundamental importance in drug design. They further discuss the impact of the potential energy