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



*


Corresponding authors



a



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


E-mail:
john.chodera@choderalab.org, wangyq@wangyq.net



b



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


E-mail:
takaba.kb@om.asahi-kasei.co.jp



c



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



d



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



e



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



f



Open Molecular Software Foundation, Davis, CA 95618, USA



g



Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA



h



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



i



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



j



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

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