Aug 2026· Journal of Chemical Theory and Computation· Vol 22, pp. 8390 - 8408· 0 citations· 110 references
Medicine
TL;DR
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.
Abstract
The application of machine learning potentials (MLPs) to accurately simulate enzymatic reactions remains challenging. This is primarily due to the high dimensionality and structural heterogeneity of enzyme systems, as well as the need to incorporate off-equilibrium and transition state conformations into the training data. Integrating MLPs into a quantum mechanical/molecular mechanical (QM/MM) framework through either direct learning or Δ-learning, together with reaction-specific training strategies, can help overcome these limitations. In this work, we present 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. Developed within CHARMM and tightly integrated with the mlp_qmmm Python package, the workflow automates training-data generation, data sanitization, MLP/ΔMLP training, and deployment of trained models in QM/MM molecular dynamics (MD) simulations. In addition, a low-overhead interface implemented in CHARMM enables efficient model inference on both CPU and GPU during simulations. The workflow further incorporates an iterative model refinement strategy that systematically improves predictive performance through successive rounds of sampling, high-level labeling, and retraining. The capabilities of the approach are demonstrated using the hydride-transfer reaction catalyzed by four variants of dihydrofolate reductase (DHFR). Compared with conventional ai/DFT-QM/MM simulations, the ΔMLP-assisted approach achieves more than 500-fold acceleration while maintaining subkcal/mol accuracy in predicted reaction free energies and free energy barriers. Iterative refinement further improves the underlying energy and force predictions, leading to more accurate thermodynamic and structural properties obtained from QM/MM simulations with only a modest additional computational cost. Overall, this work establishes a scalable and extensible workflow for the systematic development and iterative refinement of reaction-specific MLP/ΔMLP models, enabling highly accurate and computationally efficient simulations of enzyme-catalyzed reactions.
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
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
This Perspective surveys the central methodological challenges in developing ML/MM frameworks, including the generation of high-quality reference data and the treatment of multiscale coupling.
Xinhu Sha, Chenyu Wu, Daiqian Xie et al.· Journal of Physical Chemistr...· 0 citations
Thermodynamic quantities in the hydrated state provide essential reference information for understanding hydrothermal biomass decomposition. However, accurately estimating thermodynamic quantities in the hydrated state using computational methods remains challenging. In this study, we develop a general computational framework to evaluate accurate solution‐phase thermodynamics in the standard state for compounds involved in the decomposition of xylose and glucose in water by combining gas‐phase free‐energy calculations with machine‐learning prediction of hydration free energies. Gas‐phase thermodynamic quantities are evaluated by a composite method designed to reproduce coupled‐cluster theory with singles, doubles, and perturbative triples in the complete basis set limit (CCSD(T)/CBS) accuracy. Hydration free energies are predicted using molecular descriptors and machine learning. The resulting approach enables efficient evaluation of large reaction networks and is demonstrated for a dataset comprising 84 molecules and 73 reactions. Comparison with experimentally‐derived free‐energy differences for sugar isomerization indicates that the method achieves an accuracy within 3.7 kcal/mol in aqueous solution. The computed thermodynamic data further identify furfural and 5‐hydroxymethylfurfural as thermodynamically favorable products in the xylose and glucose systems, respectively. This framework provides a practical route to accurate standard‐state solution‐phase thermodynamics by combining high‐accuracy gas‐phase thermochemistry with machine‐learning prediction of hydration free energies.
Mikito Fujinami, Akihiko Okubo, Shuhei Ogo et al.· Journal of Computational Che...· 0 citations
Melting point (MP) is an important thermophysical property for the chemical process industry, yet accurate prediction of MP for organic compounds in the absence of experimental data remains challenging due to the complex interplay between molecular packing, intermolecular interactions, and electronic structure. Traditional group contribution and quantitative structure-property relationship models, which rely primarily on static molecular descriptors, often fail to capture these critical condensed-phase effects. In this study, we present a hybrid machine learning framework that integrates cheminformatics descriptors with quantum chemical features and dynamic condensed-phase descriptors derived from molecular dynamics (MD) simulations. Using a curated subset of the DIPPR 801 database, multiple machine learning architectures, including light gradient boosting machine (LightGBM) and graph convolutional networks, were evaluated with feature sets of increasing physical fidelity. The best-performing model, based on LightGBM trained on Dragon descriptors augmented with MD and quantum chemical features, achieves a mean absolute error of 22.5 K, outperforming descriptor-only models and structure-based deep learning baselines. Shapley additive explanations interpretability analysis reveals that melting behavior is governed primarily by molecular topology, surface-area-weighted electronic descriptors, and condensed-phase interaction properties. In contrast, many isolated functional group and single molecule electronic descriptors contribute negligibly once these effects are accounted for. These results demonstrate that incorporating physics-informed, multi-scale descriptors enables more accurate and physically interpretable MP predictions.
Frank T. Mtetwa, N. Giles, W. Wilding et al.· Journal of Chemical Physics· 0 citations
This work not only establishes a pioneering paradigm for interpretable ML-driven force field refinement but also provides the first feature engineering solution incorporating chemical, physical, and structural information specifically designed for the machine learning of energetic molecular crystals.
Qi He, Pengju Wang, Xudong He et al.· Molecules· 0 citations
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