Role of Molecular Dynamics and Related Methods in Drug Discovery

Abstract

Molecular dynamics (MD) and related methods are close to becoming routine computational tools for drug discovery. Their main advantage is in explicitly treating structural flexibility and entropic effects. This allows a more accurate estimate of the thermodynamics and kinetics associated with drug-target recognition and binding, as better algorithms and hardware architectures increase their use. Here, we review the theoretical background of MD and enhanced sampling methods, focusing on free-energy perturbation, metadynamics, steered MD, and other methods most consistently used to study drug-target binding. We discuss unbiased MD simulations that nowadays allow the observation of unsupervised ligand-target binding, assessing how these approaches help optimizing target affinity and drug residence time toward improved drug efficacy. Further issues discussed include allosteric modulation and the role of water molecules in ligand binding and optimization. We conclude by calling for more prospective studies to attest to these methods' utility in discovering novel drug candidates.

Computer-aided drug design: time to play with novel chemical matter

“…While it is possible to identify the association pathway and predict binding rates [9], this can only be done for one or a handful of ligands. On a small set of congeneric ligands, MD can provide the necessary sampling to make accurate relative free energy predictions [10]. But, by focusing on a specific interaction, the recently described Dynamic Undocking (DUck) method has proven remarkably successful at discriminating between active and inactive compounds.…”

“…The GPUs have created new possibilities, such as direct observation of the protein-ligand association process [9]. But, perhaps more importantly, as MD provides essential configurational sampling for many advanced structure-based computational methods, GPUs enable the practical application of free energy calculations and other drug discovery applications [10]. We are starting to see GPU implementations of other computational chemistry methods, ranging all the way from chemoinformatics to quantum mechanics.…”

Antibodyomics: bioinformatics technologies for understanding B‐cell immunity to HIV‐1

“…As discussed in the prior section, MD simulations are widely used to define the conformational space of macromolecules,83 and free energy perturbation (FEP) methodology constitutes one of the most rigorous MD‐based approaches to determine the binding free energy for complex biological systems 93. FEP exploits the fact that free energy is a state function, so that the difference in free energy between two states does not depend on the path between the two states.…”

Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration

“…3,7,11-15 These methods include Thermodynamic Integration (TI) 11,16-18 and Free Energy Perturbation (FEP), 11,19-22 as well as analysis through Bennett’s acceptance ratio and its variants (BAR/MBAR) 23-28 and are augmented with enhanced sampling such as replica exchange molecular dynamics, 29 metadynamics, 30 driven adiabatic free energy dynamics, 31 orthogonal space random walk, 32 adaptive integration, 33 and other methods. 34,35 Advanced alchemical free energy methods for binding affinity prediction 9,10,36 have evolved to the point that they approach quantitative predictive accuracy for ligand-binding affinities, 27,37,38 although much work remains, such as addressing sampling and convergence issues, 27,37 to improve precision so as to afford meaningful comparison with experimental uncertainties. 39 …”