Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor.

ABSTRACT: The retrieval of hit/lead compounds with novel scaffolds during early drug development is an important but challenging task. Various generative models have been proposed to create drug-like molecules. However, the capacity of these generative models to design wet-lab-validated and target-specific molecules with novel scaffolds has hardly been verified. We herein propose a generative deep learning (GDL) model, a distribution-learning conditional recurrent neural network (cRNN), to generate tailor-made virtual compound libraries for given biological targets. The GDL model is then applied to RIPK1. Virtual screening against the generated tailor-made compound library and subsequent bioactivity evaluation lead to the discovery of a potent and selective RIPK1 inhibitor with a previously unreport

SUBMITTER: Li Y 

PROVIDER: S-EPMC9653409 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature