Researchers have introduced DrugGen-2, a generative model designed to create small molecules conditioned on both disease ontology and target protein sequences. This approach addresses the gap in current computational methods by integrating disease context into the molecular generation process.
- Developed by fine-tuning a pre-trained GPT-2 model using supervised fine-tuning followed by reinforcement learning via group relative policy optimization (GRPO).
- Optimized with reward functions for chemical validity, novelty, diversity, and high predicted binding affinity.
- Evaluated on five protein targets relevant to diabetic nephropathy, significantly outperforming baseline models DrugGPT and DrugGen.
- Demonstrated superior capacity to generate unique molecules with greater structural similarity to approved drugs.
- Achieved improved predicted binding affinities, including compounds with affinities of -9.917, -9.485, and -9.367 exceeding the reference drug enalapril (-8.283).
By integrating disease-specific context, DrugGen-2 advances AI-assisted drug discovery, offering a powerful tool for de novo design and drug repurposing that accounts for the complex interplay between diseases and molecular targets.