End-to-end differentiable construction of molecular mechanics force fields-Reference-Cited by-同舟云学术

End-to-end differentiable construction of molecular mechanics force fields

Published:2022 Issue:41 Volume:13 Page:12016-12033
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Wang Yuanqing¹²³^ORCID,Fass Josh¹⁴^ORCID,Kaminow Benjamin¹⁴^ORCID,Herr John E.¹^ORCID,Rufa Dominic¹⁵^ORCID,Zhang Ivy¹⁴^ORCID,Pulido Iván¹^ORCID,Henry Mike¹^ORCID,Bruce Macdonald Hannah E.¹^ORCID,Takaba Kenichiro¹⁶^ORCID,Chodera John D.¹^ORCID

Affiliation:

1. Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA

2. Physiology, Biophysics and System Biology PhD Program, Weill Cornell Medical College, Cornell University, New York 10065, NY, USA

3. MFA Program in Creative Writing, Division of Humanities and Arts, City College of New York, City University of New York, New York 10031, NY, USA

4. Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York 10065, NY, USA

5. Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York 10065, NY, USA

6. Pharmaceutical Research Center, Advanced Drug Discovery, Asahi Kasei Pharma Corporation, Shizuoka 410-2321, Japan

Abstract

Graph neural network-based continuous embedding is used to replace a human expert-derived discrete atom typing scheme to parametrize accurate and extensible molecular mechanics force fields.

Funder

National Science Foundation

National Institutes of Health

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SC/D2SC02739A

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