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Quantum Machine Learning Quantum Chemistry Quantum Simulation

Machine Learning/Molecular Mechanics Enzymology for the Next Generation of Computational Enzymatic Catalysis.

PubMed
Authors: Wang X, Wang J, Li WL

Year

2026

Paper ID

38603

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

98

Citations

0

Abstract

While quantum mechanics/molecular mechanics (QM/MM) frameworks have long enabled simulations of chemical reactivity, recent advances in machine learning interatomic potentials (MLIPs) have extended these capabilities by providing near-quantum accuracy at molecular mechanics efficiency. Embedded within machine learning/molecular mechanics (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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • While quantum mechanics/molecular mechanics (QM/MM) frameworks have long enabled simulations of chemical reactivity, recent advances in machine learning interatomic potentials...

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References & Citation Signals

[1] DOI https://doi.org/10.1016/j.checat.2026.101658 [2] Publisher https://pubmed.ncbi.nlm.nih.gov/41907567/

Local Citation Graph (Related-Paper Links)

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External citation index: OpenAlex citation signal • updated 2026-06-13 22:56:20

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