Computational design of CDK1 inhibitors with enhanced target affinity and drug-likeness using deep-learning framework.

ABSTRACT: Cyclin Dependent Kinase 1 (CDK1) plays a crucial role in cell cycle regulation, and dysregulation of its activity has been implicated in various cancers. Although several CDK1 inhibitors are currently in clinical trials, none have yet been approved for therapeutic use. This research utilized deep learning techniques, specifically Recurrent Neural Networks with Long Short-Term Memory (LSTM), to generate potential CDK1 inhibitors. Molecular docking, evaluation of molecular properties, and molecular dynamics simulations were conducted to identify the most promising candidates. The results showed that the generated ligands exhibited substantial improvements in target affinity and drug-likeness. Molecular docking results showed that the generated ligands had an average binding affinity of -10.6

SUBMITTER: Lu Z 

PROVIDER: S-EPMC11693894 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature