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TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics.

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Bibliographic Details
Title:	TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics.
Authors:	Rai BK; Simulation and Modeling Sciences, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States., Sresht V; Simulation and Modeling Sciences, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States., Yang Q; Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States., Unwalla R; Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States., Tu M; Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States., Mathiowetz AM; Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States., Bakken GA; Digital, Pfizer, Eastern Point Road, Groton, Connecticut 06340, United States.
Source:	Journal of chemical information and modeling [J Chem Inf Model] 2022 Feb 28; Vol. 62 (4), pp. 785-800. Date of Electronic Publication: 2022 Feb 04.
Publication Type:	Journal Article; Research Support, Non-U.S. Gov't
Journal Info:	Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	1549-960X
DOI:	10.1021/acs.jcim.1c01346