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DrugGen-2 is a generative model designed to enhance drug discovery by incorporating disease context alongside target protein sequences, addressing a critical gap in traditional drug design approaches. Utilizing a two-step training process that combines supervised fine-tuning and reinforcement learning via group relative policy optimization, DrugGen-2 was trained on a dataset linking approved drugs to their respective diseases and targets. The model significantly outperformed existing baselines in generating unique molecules with high predicted binding affinities, demonstrating its potential to improve therapeutic outcomes in drug discovery, particularly for diabetic nephropathy.
DrugGen-2 redefines drug discovery by integrating disease context, yielding molecules with binding affinities that surpass traditional reference drugs.
Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes. To address this gap, we introduce DrugGen-2, a novel generative model that designs small molecules conditioned on both disease ontology and target protein sequences. DrugGen-2 was developed by fine-tuning a pre-trained GPT-2 model on a curated dataset of approved drugs linked to their diseases and targets, using a two-step strategy of supervised fine-tuning followed by reinforcement learning via group relative policy optimization (GRPO). This process was guided by reward functions optimizing for chemical validity, novelty, diversity, and high predicted binding affinity. When evaluated on five protein targets relevant to diabetic nephropathy, DrugGen-2 significantly outperformed baseline models (DrugGPT and DrugGen). It demonstrated a superior capacity to generate unique molecules, exhibited greater structural similarity to approved drugs, and achieved improved predicted binding affinities across all targets. Molecular docking analyses further supported these findings, identifying candidate ligands with strong binding potential, including compounds with predicted affinities (-9.917, -9.485, and -9.367) exceeding those of reference drugs such as enalapril for angiotensin-converting enzyme (-8.283). By integrating disease-specific context into molecular generation, 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.