Harnessing Artificial Intelligence to Understand Enzyme Catalysis and Discover New Enzymes
- 1School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA
- 2School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- 3Department of Chemistry, Lund University, Lund, Sweden
Abstract
Molecular simulations have been central to our understanding of how enzymes work, and the ongoing explosion of artificial intelligence/ML (AI/ML) approaches is greatly expanding the scope of problems that can be addressed using simulations. The speed of development of the field is tremendous, with the near-constant development of new approaches and successful applications. This review aims to give a higher-level overview of four key areas where AI/ML approaches are significantly transforming computational enzymology (both understanding and design), specifically (1) enzyme discovery and functional annotation, (2) kinetic parameter prediction, (3) understanding of protein dynamics and enzyme reaction mechanisms, and (4) decoding protein allostery. We also discuss some of the outstanding technical challenges in this research space, and efforts being made to overcome them.
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BibTeX
@article{brownless2026harnessing,
title = {{Harnessing Artificial Intelligence to Understand Enzyme Catalysis and Discover New Enzymes}},
author = {Alfie-Louise R. Brownless and Shina C. L. Kamerlin},
journal = {Current opinion in structural biology},
year = {2026},
month = oct,
doi = {10.1016/j.sbi.2026.103422},
url = {https://doi.org/10.1016/j.sbi.2026.103422},
note = {CC BY},
}
RIS
TY - JOUR
TI - Harnessing Artificial Intelligence to Understand Enzyme Catalysis and Discover New Enzymes
AU - Alfie-Louise R. Brownless
AU - Shina C. L. Kamerlin
PY - 2026
DA - 2026/10/29
JO - Current opinion in structural biology
DO - 10.1016/j.sbi.2026.103422
UR - https://doi.org/10.1016/j.sbi.2026.103422
AB - Molecular simulations have been central to our understanding of how enzymes work, and the ongoing explosion of artificial intelligence/ML (AI/ML) approaches is greatly expanding the scope of problems that can be addressed using simulations. The speed of development of the field is tremendous, with the near-constant development of new approaches and successful applications. This review aims to give a higher-level overview of four key areas where AI/ML approaches are significantly transforming computational enzymology (both understanding and design), specifically (1) enzyme discovery and functional annotation, (2) kinetic parameter prediction, (3) understanding of protein dynamics and enzyme reaction mechanisms, and (4) decoding protein allostery. We also discuss some of the outstanding technical challenges in this research space, and efforts being made to overcome them.
SN - 0959-440X
SN - 1879-033X
ER -