Accelerating drug target inhibitor discovery with a deep generative foundation model.

ABSTRACT: Inhibitor discovery for emerging drug-target proteins is challenging, especially when target structure or active molecules are unknown. Here, we experimentally validate the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions-unbiased toward any specific target. We performed a protein sequence-conditioned sampling on the generative foundation model to design small-molecule inhibitors for two dissimilar targets: the spike protein receptor-binding domain (RBD) and the main protease from SARS-CoV-2. Despite using only the target sequence information during the model inference, micromolar-level inhibition was observed in vitro for two candidates out of four synthesized for each target. The most potent spike RBD inhib

SUBMITTER: Chenthamarakshan V 

PROVIDER: S-EPMC10284550 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature