What question did this study set out to answer?

The aim is to explore how machine learning interatomic potentials enhance enzymatic simulations within quantum and molecular mechanics frameworks.

March 7, 2026Open Access

Machine learning/molecular mechanics enzymology for the next generation of computational enzymatic catalysis

Key Points

The aim is to explore how machine learning interatomic potentials enhance enzymatic simulations within quantum and molecular mechanics frameworks.
Summarized datasets and training strategies for reactive machine learning interatomic potentials (MLIPs).
Reviewed recent developments in ML/MM-based enzymatic simulations.
Discussed future challenges and opportunities for more complex simulations.
Established that MLIPs provide near-quantum accuracy with molecular mechanics efficiency.
Identified the ability of ML frameworks to enable large-scale simulations previously deemed impractical.
Highlighted opportunities for expanding simulation capabilities in complex scenarios.

Abstract

Summary Although hybrid quantum mechanics and molecular mechanics (QM/MM) frameworks have long enabled simulations of chemical reactivity, recent advances in machine learning (ML) interatomic potentials (MLIPs) have extended these capabilities by providing near-quantum accuracy at MM efficiency. Embedded within ML/MM frameworks, MLIPs enable large-scale reactive simulations that were previously impractical with conventional QM/MM approaches. This perspective summarizes the datasets and training strategies used for reactive MLIPs, reviews recent progress in ML/MM-based enzymatic simulations, and discusses their potential extension to more complex scenarios, thereby identifying key opportunities and challenges for future research and applications.

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