ΔNAzyme: A Foundational QM/MM Δ-Machine Learning Potential for Nucleic Acid Enzyme Catalysis
- DOI
- 10.1021/acs.jctc.6c01635
- Published
- 2026-10-12
- Container
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jctc.6c01635,
title = {ΔNAzyme:
A Foundational QM/MM Δ-Machine
Learning Potential for Nucleic Acid Enzyme Catalysis},
author = {Erika McCarthy and Timothy J. Giese and Şölen Ekesan and Julie Puyo-Fourtine and Lauren Lerew and Andreas W. Götz and Darrin M. York},
year = {2026},
journal = {Journal of Chemical Theory
and Computation},
doi = {10.1021/acs.jctc.6c01635},
url = {https://doi.org/10.1021/acs.jctc.6c01635}
}RIS
TY - JOUR TI - ΔNAzyme: A Foundational QM/MM Δ-Machine Learning Potential for Nucleic Acid Enzyme Catalysis AU - Erika McCarthy AU - Timothy J. Giese AU - Şölen Ekesan AU - Julie Puyo-Fourtine AU - Lauren Lerew AU - Andreas W. Götz AU - Darrin M. York PY - 2026 JO - Journal of Chemical Theory and Computation DO - 10.1021/acs.jctc.6c01635 UR - https://doi.org/10.1021/acs.jctc.6c01635 ER -
APA
McCarthy, E., Giese, T. J., Ekesan, S., Puyo-Fourtine, J., Lerew, L., Götz, A. W., & York, D. M. (2026). ΔNAzyme: A Foundational QM/MM Δ-Machine Learning Potential for Nucleic Acid Enzyme Catalysis. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.6c01635
Source records
- crossref · retrieved 2026-11-09T17:10:09.372Z