Jul 2026· Chemical Science· Vol 17, pp. 16108-16150· 0 citations· 390 references
Medicine
TL;DR
Recent developments in ML potentials, ML collective variables, and committor-based sampling are presented as innovative methods that have been able to address some of the current challenges in accuracy, sampling efficiency, and the identification of low-dimensional representations of reaction coordinates.
Abstract
We offer a practical and conceptual introduction to some of the current approaches to modelling enzymatic reaction mechanisms, ranging from quantum mechanics (QM), molecular mechanics (MM), and hybrid QM/MM approaches to enhanced sampling methods, knowledge-based approaches, and machine learning (ML) advances. We discuss how static and dynamic QM/MM approaches, as well as multi-PES strategies, have contributed to understanding the role of conformational diversity, electrostatic preorganization and solvent participation in the determination of catalytic barriers and reaction paths. We focus on how advanced sampling techniques and data-driven collective variables have enabled the exploration of rare events and reaction coordinates, as well as how knowledge- and rule-based approaches have facilitated the interpretation and hypothesis generation for different families of enzymes. Recent developments in ML potentials, ML collective variables, and committor-based sampling are presented as innovative methods that have been able to address some of the current challenges in accuracy, sampling efficiency, and the identification of low-dimensional representations of reaction coordinates. A case study of α-amylase demonstrates how the combination of these strategies leads to a comprehensive understanding of enzyme reactivity, from the chemical to the conformational level. Collectively, these developments contribute to a predictive understanding of enzymatic catalysis, which will have extensive implications in enzyme engineering, sustainable chemistry, and drug discovery. Advances in high performance computing, automated simulation pipelines and data formats will likely make multiscale simulation more accessible and reproducible. Simultaneously, the combined application of mechanistic knowledge, ML, and experimental validation will hopefully advance the discovery and optimization of biocatalysts with well-defined properties, tailored to meet pressing societal needs, such as plastic biodegradation, carbon sequestration, sustainable synthesis and personalised medicine.
A physics-based framework for the relationship between the electrostatic energy and the electrostatic field generated by the MM environment, MIREANN exhibits powerful transferability in reproducing the free energy barriers predicted by QM/MM-MD with errors less than 0.5 kcal mol–1 for various enzyme variants without the need to retrain the MLP.
Xinhu Sha, Xuehui Guo, Zi-Yu Chen et al.· Journal of Chemical Theory a...· 0 citations
Predicting how mutations alter enzyme catalysis remains a central challenge in enzymology and enzyme engineering. Although QM/MM simulations can in principle compute the activation free energy associated with enzymatic reactions, their high computational cost limits systematic studies across many variants. Here, we benchmark a mechanical-embedding machine learning potential/molecular mechanics (ML/MM) protocol for predicting mutation effects on chorismate mutase catalysis, a model system extensively studied both experimentally and computationally. In this framework, the QM-region potential energy surface is represented by an actively learned machine-learning potential, while QM/MM electrostatic interactions are treated classically using partial charges predicted from instantaneous geometries for the QM region. Combined with umbrella sampling, the ML/MM approach enables efficient estimation of activation free energies and direct comparison with experimental kinetics. The method shows reasonable correlations with experiment across both nonpolar and polar active-site mutations and is quantitatively accurate for nonpolar mutations despite their narrow energetic range (< 1 kcal mol−1). However, it substantially underestimates the activation free energy for polar mutations. The results highlight both the promise and limitations of mechanical-embedding ML/MM approaches for predicting mutation effects on enzyme catalysis.
Zi-Chen Sun, Yi-Fan Li, Wenqiang Cui et al.· Journal of Chemical Theory a...· 0 citations
An integrated workflow for MLP/ΔMLP-assisted QM/MM simulations that achieves ab initio (ai) or density functional theory (DFT)-level accuracy in predicting enzyme reaction thermodynamics and incorporates an iterative model refinement strategy that systematically improves predictive performance through successive rounds of sampling, high-level labeling, and retraining.
Abdul Raafik Arattu Thodika, S. Panda, Xiao-Liang Pan et al.· Journal of Chemical Theory a...· 0 citations
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.
S. S. Çınaroğlu· Mini-Reviews in Medical Chem...· 0 citations
It is argued that, since physics-based simulations and machine learning provide complementary approximations to the underlying probability distribution associated with biomolecular recognition events, and they excel respectively in consistency with free-energy landscapes and state populations and in predictive accuracy, the central challenge for the coming decade will be integrating them into hybrid frameworks that are scalable and transferable.
R. Khalil, Elena Frasnetti, Han Kurt et al.· Journal of Physical Chemistr...· 0 citations
Electronic rearrangements associated with bond forming/breaking in catalytic enzymes require quantum mechanical (QM) treatment beyond classical molecular mechanics (MM). Hybrid QM/MM methods enable tractable simulations but require system-specific setup and are sensitive to the QM region choice and treatment of the QM/MM interface. We demonstrate quantum-accurate treatment of all-atom, complete enzymes in explicit solvent comprising up to 54k atoms and 1 microsecond of total simulation time using the machine-learned interatomic potential (MLIP) eSEN-omol. We reproduce experimental barrier trends for Claisen rearrangement in chorismate mutase, resolve critical intermediate states in PETase catalyzed polymer depolymerization, and distinguish mechanistic alternatives for metal-activated phosphoryl transfer in nucleoside diphosphate kinase. We realize 1000x speedups relative to typical QM/MM calculations without system-specific tuning. These results establish MLIPs as a practical route to QM-accurate simulations of enzyme catalysis.
Meng Gao, Armin Shayesteh Zadeh, Aniruddha Seal et al.· 0 citations
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