Accelerated Discovery of Carbamate Cbl-b Inhibitors Using Generative AI Models and Structure-Based Drug Design.
Journal
Journal of medicinal chemistry
ISSN: 1520-4804
Titre abrégé: J Med Chem
Pays: United States
ID NLM: 9716531
Informations de publication
Date de publication:
12 Aug 2024
12 Aug 2024
Historique:
medline:
12
8
2024
pubmed:
12
8
2024
entrez:
12
8
2024
Statut:
aheadofprint
Résumé
Casitas B-lymphoma proto-oncogene-b (Cbl-b) is a RING finger E3 ligase that has an important role in effector T cell function, acting as a negative regulator of T cell, natural killer (NK) cell, and B cell activation. A discovery effort toward Cbl-b inhibitors was pursued in which a generative AI design engine, REINVENT, was combined with a medicinal chemistry structure-based design to discover novel inhibitors of Cbl-b. Key to the success of this effort was the evolution of the "Design" phase of the Design-Make-Test-Analyze cycle to involve iterative rounds of an in silico structure-based drug design, strongly guided by physics-based affinity prediction and machine learning DMPK predictive models, prior to selection for synthesis. This led to the accelerated discovery of a potent series of carbamate Cbl-b inhibitors.
Identifiants
pubmed: 39132828
doi: 10.1021/acs.jmedchem.4c01034
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM