Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking.
Fiche publication
Date publication
octobre 2022
Journal
Journal of medicinal chemistry
Auteurs
Membres identifiés du Cancéropôle Est :
Pr HIBERT Marcel, Dr VILLA Pascal
Tous les auteurs :
Eguida M, Schmitt-Valencia C, Hibert M, Villa P, Rognan D
Lien Pubmed
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.
Référence
J Med Chem. 2022 10 18;: