Machine-learned molecular mechanics force fields from large-scale quantum chemical data

Author:

Takaba Kenichiro12ORCID,Friedman Anika J.3ORCID,Cavender Chapin E.4ORCID,Behara Pavan Kumar5ORCID,Pulido Iván1ORCID,Henry Michael M.1ORCID,MacDermott-Opeskin Hugo6ORCID,Iacovella Christopher R.1ORCID,Nagle Arnav M.17ORCID,Payne Alexander Matthew18ORCID,Shirts Michael R.3ORCID,Mobley David L.9ORCID,Chodera John D.1ORCID,Wang Yuanqing101ORCID

Affiliation:

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

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

3. Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA

4. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA

5. Center for Neurotherapeutics, Department of Pathology and Laboratory Medicine, University of California, Irvine, CA 92697, USA

6. Open Molecular Software Foundation, Davis, CA 95618, USA

7. Department of Bioengineering, University of California, Berkeley, Berkeley, CA, 94720, USA

8. Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York 10065, USA

9. Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, USA

10. Simons Center for Computational Physical Chemistry and Center for Data Science, New York University, New York, NY 10004, USA

Abstract

A generalized and extensible machine-learned molecular mechanics force field trained on over 1.1 million QC data applicable for drug discovery applications. Figure reproduced from the arXiv:201001196 preprint under the arXiv non-exclusive license.

Funder

National Science Foundation

National Institutes of Health

New York University

Publisher

Royal Society of Chemistry (RSC)

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