Open Access

JACS Au, volume 4, issue 4, pages 1374-1384

Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines

Ruibin Liu ¹

Joseph Clayton ^{1, 2}

Mingzhe Shen ¹

Shubham Bhatnagar ³

Jana Shen ¹

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Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States |

Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States |

Department of Computer Science, University of Maryland at College Park, College Park, Maryland 20742, United States |

Publication type: Journal Article

Publication date: 2024-04-05

American Chemical Society (ACS)

Journal: JACS Au

scimago Q1

SJR: 2.937

CiteScore: 9.1

Impact factor: 8.5

ISSN: 26913704

DOI: 10.1021/jacsau.3c00749

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Organic Chemistry

Physical and Theoretical Chemistry

Analytical Chemistry

Chemistry (miscellaneous)

Found

	1 2
Journal of Chemical Information and Modeling	Journal of Chemical Information and Modeling, 2, 33.33% Journal of Chemical Information and Modeling 2 publications, 33.33%
Expert Opinion on Drug Discovery	Expert Opinion on Drug Discovery, 1, 16.67% Expert Opinion on Drug Discovery 1 publication, 16.67%
Cell Chemical Biology	Cell Chemical Biology, 1, 16.67% Cell Chemical Biology 1 publication, 16.67%
Journal of Chemical Theory and Computation	Journal of Chemical Theory and Computation, 1, 16.67% Journal of Chemical Theory and Computation 1 publication, 16.67%
	1 2

	1 2 3
American Chemical Society (ACS)	American Chemical Society (ACS), 3, 50% American Chemical Society (ACS) 3 publications, 50%
Taylor & Francis	Taylor & Francis, 1, 16.67% Taylor & Francis 1 publication, 16.67%
Elsevier	Elsevier, 1, 16.67% Elsevier 1 publication, 16.67%
Cold Spring Harbor Laboratory	Cold Spring Harbor Laboratory, 1, 16.67% Cold Spring Harbor Laboratory 1 publication, 16.67%
	1 2 3

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Publisher

American Chemical Society (ACS)

Journal

JACS Au

scimago Q1

SJR

2.937

CiteScore

9.1

Impact factor

8.5

ISSN

26913704 (Print, Electronic)

Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines

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