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Review

Molecular Dynamics Simulations in Modern Medicinal Chemistry.

Aug 2026 · Mini-Reviews in Medical Chemistry · Vol 26 · 1 citation
Medicine

TL;DR

A practical overview of classical atomistic MD methodologies commonly used in medicinal chemistry, including force-field-based simulations, enhanced sampling techniques, and free-energy calculation methods such as alchemical and end-point approaches are provided.

Abstract

Molecular dynamics (MD) simulations have become an increasingly important component of modern medicinal chemistry and structure-based drug discovery, providing atomistic insight into protein-ligand interactions that extends beyond static experimental structures and docking models. By explicitly accounting for conformational flexibility, solvent effects, and time-dependent behaviour, MD simulations enable the refinement of binding poses, the identification of transient and allosteric sites, and the quantitative estimation of binding thermodynamics and kinetics, the latter increasingly accessible through Markov state models (MSMs) and milestoning approaches that reconstruct long-timescale behaviour from ensembles of short trajectories. In this mini-review, we provide a practical overview of classical atomistic MD methodologies commonly used in medicinal chemistry, including force-field-based simulations, enhanced sampling techniques, and free-energy calculation methods such as alchemical and end-point approaches. Emphasis is placed on the strengths and limitations of each technique, with particular attention to their appropriate use across different stages of the drug discovery pipeline. We further discuss best practices for system preparation, simulation protocol design, convergence assessment, and reproducibility, highlighting common pitfalls that can lead to overinterpretation of simulation results. Selected examples illustrate how MD simulations have informed medicinal chemistry decisions in lead identification and optimisation. Finally, we briefly outline emerging directions, including the integration of machine learning, ensemble-based approaches, and next-generation force fields, which are expected to further expand the role of MD simulations in medicinal chemistry.

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