MolOrgGPT: <i>De Novo</i> Generation via Large Language Models and Reinforcement Learning

We present a general framework for the <i>de novo</i> design of small molecules with desirable chemical properties, developed to aid the creation of novel chemical entities with potential therapeutic use. The system is built upon a foundational Large Language Model trained on a large comprehensive chemical database capable of generating structurally diverse and synthetically accessible compounds. It is then fine-tuned through reinforcement learning to enhance its capacity to generate molecules tailored to specific biological targets. As a case study, we apply this framework to design molecules targeting key proteins involved in Alzheimer's disease. The generated compounds underwent molecular docking studies to assess their binding affinities and prioritize candidates with optimal predicted interactions. The top-ranked molecules were further analyzed based on their binding modes and key molecular interactions with the target proteins. The results suggest that our generative model produces viable, drug-like molecules with favorable interactions, underscoring its potential as a valuable tool in early stage drug discovery.