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Peer-reviewedCC BY 4.0Chemical Science

Simulating enzyme catalysis with electrostatically embedded machine learning potentials

  • Valentin Gradisteanu
  • Elliot W Chan
  • Lester Hedges
  • Meritxell Malagarriga
  • Rolf David
  • Miguel de la Puente
  • Damien Laage
  • Iñaki Tuñón
  • Marc W van der Kamp
  • Kirill Zinovjev
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Abstract

Enzyme catalysis can be simulated accurately and efficiently by coupling machine-learned potentials trained on gas-phase data to the environment (ML/MM) using electrostatic machine-learning embedding (EMLE).

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Cite

BibTeXRIS
BibTeX
@article{gradisteanu2026simulating,
  title = {{Simulating enzyme catalysis with electrostatically embedded machine learning potentials}},
  author = {Valentin Gradisteanu and Elliot W Chan and Lester Hedges and Meritxell Malagarriga and Rolf David and Miguel de la Puente and Damien Laage and Iñaki Tuñón and Marc W van der Kamp and Kirill Zinovjev},
  journal = {Chemical Science},
  year = {2026},
  month = oct,
  doi = {10.1039/d6sc01156j},
  url = {https://ens.hal.science/hal-05804793v1},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Simulating enzyme catalysis with electrostatically embedded machine learning potentials
AU  - Valentin Gradisteanu
AU  - Elliot W Chan
AU  - Lester Hedges
AU  - Meritxell Malagarriga
AU  - Rolf David
AU  - Miguel de la Puente
AU  - Damien Laage
AU  - Iñaki Tuñón
AU  - Marc W van der Kamp
AU  - Kirill Zinovjev
PY  - 2026
DA  - 2026/10/18
JO  - Chemical Science
DO  - 10.1039/d6sc01156j
UR  - https://ens.hal.science/hal-05804793v1
AB  - Enzyme catalysis can be simulated accurately and efficiently by coupling machine-learned potentials trained on gas-phase data to the environment (ML/MM) using electrostatic machine-learning embedding (EMLE).
SN  - 2041-6520
SN  - 2041-6539
ER  -