Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines-Reference-Cited by-同舟云学术

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

Published:2024-04-05 Issue:4 Volume:4 Page:1374-1384
ISSN:2691-3704
Container-title:JACS Au
language:en
Short-container-title:JACS Au

Author:

Liu Ruibin¹,Clayton Joseph¹²^ORCID,Shen Mingzhe¹^ORCID,Bhatnagar Shubham³,Shen Jana¹^ORCID

Affiliation:

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

2. 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

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

Funder

National Cancer Institute

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/jacsau.3c00749

Reference46 articles.

1. Covalent inhibitors in drug discovery: from accidental discoveries to avoided liabilities and designed therapies

2. Emerging and Re-Emerging Warheads for Targeted Covalent Inhibitors: Applications in Medicinal Chemistry and Chemical Biology

3. Fragment-based covalent ligand discovery

4. K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions

5. The Ascension of Targeted Covalent Inhibitors

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