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

Autor:	Takaba K; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA john.chodera@choderalab.org wangyq@wangyq.net.; Pharmaceuticals Research Center, Advanced Drug Discovery, Asahi Kasei Pharma Corporation Shizuoka 410-2321 Japan takaba.kb@om.asahi-kasei.co.jp., Friedman AJ; Department of Chemical and Biological Engineering, University of Colorado Boulder Boulder CO 80309 USA., Cavender CE; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego 9500 Gilman Drive La Jolla CA 92093 USA., Behara PK; Center for Neurotherapeutics, Department of Pathology and Laboratory Medicine, University of California Irvine CA 92697 USA., Pulido I; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA john.chodera@choderalab.org wangyq@wangyq.net., Henry MM; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA john.chodera@choderalab.org wangyq@wangyq.net., MacDermott-Opeskin H; Open Molecular Software Foundation Davis CA 95618 USA., Iacovella CR; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA john.chodera@choderalab.org wangyq@wangyq.net., Nagle AM; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA john.chodera@choderalab.org wangyq@wangyq.net.; Department of Bioengineering, University of California, Berkeley Berkeley CA 94720 USA., Payne AM; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA john.chodera@choderalab.org wangyq@wangyq.net.; Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center New York 10065 USA., Shirts MR; Department of Chemical and Biological Engineering, University of Colorado Boulder Boulder CO 80309 USA., Mobley DL; Department of Pharmaceutical Sciences, University of California Irvine California 92697 USA., Chodera JD; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA john.chodera@choderalab.org wangyq@wangyq.net., Wang Y; Simons Center for Computational Physical Chemistry and Center for Data Science, New York University New York NY 10004 USA.; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA john.chodera@choderalab.org wangyq@wangyq.net.
Jazyk:	angličtina
Zdroj:	Chemical science [Chem Sci] 2024 Jun 26; Vol. 15 (32), pp. 12861-12878. Date of Electronic Publication: 2024 Jun 26 (Print Publication: 2024).
DOI:	10.1039/d4sc00690a
Abstrakt:	The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. Competing Interests: J. D. C. is a current member of the Scientific Advisory Board of OpenEye Scientific Software, Redesign Science, Ventus Therapeutics, and Interline Therapeutics, and has equity interests in Redesign Science and Interline Therapeutics. The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, Vir Biotechnology, Bayer, XtalPi, Interline Therapeutics, the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, and the Sloan Kettering Institute. A complete funding history for the Chodera lab can be found at http://choderalab.org/funding. Y. W. has limited financial interests in Flagship Pioneering, Inc. and its subsidiaries. M. R. S. is an Open Science Fellow with Psivant Sciences and consults for Relay Therapeutics. D. L. M. serves on the scientific advisory boards of Anagenex and OpenEye Scientific Software, Cadence Molecular Sciences, and is an Open Science Fellow with Psivant. (This journal is © The Royal Society of Chemistry.)
Databáze:	MEDLINE
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