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

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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
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PubType: Academic Journal
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  Data: TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics.
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  Data: <searchLink fieldCode="AU" term="%22Rai+BK%22">Rai BK</searchLink>; Simulation and Modeling Sciences, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States.<br /><searchLink fieldCode="AU" term="%22Sresht+V%22">Sresht V</searchLink>; Simulation and Modeling Sciences, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States.<br /><searchLink fieldCode="AU" term="%22Yang+Q%22">Yang Q</searchLink>; Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States.<br /><searchLink fieldCode="AU" term="%22Unwalla+R%22">Unwalla R</searchLink>; Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States.<br /><searchLink fieldCode="AU" term="%22Tu+M%22">Tu M</searchLink>; Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States.<br /><searchLink fieldCode="AU" term="%22Mathiowetz+AM%22">Mathiowetz AM</searchLink>; Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States.<br /><searchLink fieldCode="AU" term="%22Bakken+GA%22">Bakken GA</searchLink>; Digital, Pfizer, Eastern Point Road, Groton, Connecticut 06340, United States.
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  Data: <searchLink fieldCode="JN" term="%22101230060%22">Journal of chemical information and modeling</searchLink> [J Chem Inf Model] 2022 Feb 28; Vol. 62 (4), pp. 785-800. <i>Date of Electronic Publication: </i>2022 Feb 04.
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        Value: 10.1021/acs.jcim.1c01346
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        Text: English
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      – TitleFull: TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics.
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              Text: 2022 Feb 28
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