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DrugGen-2 is a novel generative model that enhances drug discovery by designing small molecules based on both disease ontology and target protein sequences. By fine-tuning a pre-trained GPT-2 model with a curated dataset and employing a two-step strategy of supervised fine-tuning followed by reinforcement learning, DrugGen-2 significantly outperformed baseline models in generating unique molecules with higher predicted binding affinities. Evaluations on diabetic nephropathy targets revealed DrugGen-2's ability to produce candidate ligands with binding affinities surpassing those of established drugs, demonstrating its potential to revolutionize AI-assisted drug design.
Integrating disease context into molecular generation, DrugGen-2 outperforms existing models, yielding drug candidates with superior binding affinities.
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.
