DrugGen enhances drug discovery with large language models and reinforcement learning.

ABSTRACT: Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions to these challenges. One promising algorithm is DrugGPT, a transformer-based model, that generates small molecules for input protein sequences. Although promising, it generates both chemically valid and invalid structures and does not incorporate the features of approved drugs, resulting in time-consuming and inefficient drug discovery. To address these issues, we introduce DrugGen, an enhanced model based on the DrugGPT structure. DrugGen is fine-tuned on approved drug-target interactions and optimized with proximal policy optimization. By gi

SUBMITTER: Sheikholeslami M 

PROVIDER: S-EPMC12008224 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature