Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking.
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:
27 10 2022
27 10 2022
Historique:
pubmed:
19
10
2022
medline:
29
10
2022
entrez:
18
10
2022
Statut:
ppublish
Résumé
We here describe a computational approach (POEM: Pocket Oriented Elaboration of Molecules) to drive the generation of target-focused libraries while taking advantage of all publicly available structural information on protein-ligand complexes. A collection of 31 384 PDB-derived images with key shapes and pharmacophoric properties, describing fragment-bound microenvironments, is first aligned to the query target cavity by a computer vision method. The fragments of the most similar PDB subpockets are then directly positioned in the query cavity using the corresponding image transformation matrices. Lastly, suitable connectable atoms of oriented fragment pairs are linked by a deep generative model to yield fully connected molecules. POEM was applied to generate a library of 1.5 million potential cyclin-dependent kinase 8 inhibitors. By synthesizing and testing as few as 43 compounds, a few nanomolar inhibitors were quickly obtained with limited resources in just two iterative cycles.
Identifiants
pubmed: 36256484
doi: 10.1021/acs.jmedchem.2c00931
doi:
Substances chimiques
Ligands
0
Cyclin-Dependent Kinase 8
EC 2.7.11.22
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM