DrugGen 2: A disease-aware language model for enhancing drug discovery

2026-07-09

AI Summary

This study addresses the frequent oversight in existing drug design approaches of how disease context influences target protein behavior, often resulting in generated molecules with limited therapeutic relevance. To bridge this gap, the authors propose a novel GPT-2–based dual-condition molecular generation framework that explicitly incorporates both disease ontologies and target protein sequences into the generative process. The model is trained using supervised fine-tuning followed by group relative policy optimization (GRPO), a reinforcement learning strategy that jointly optimizes chemical validity, novelty, diversity, and high predicted binding affinity. Experiments on five targets associated with diabetic nephropathy demonstrate that the generated molecules exhibit structural similarity to approved drugs and significantly outperform baseline methods in predicted binding affinity, with several candidates surpassing enalapril—a reference drug—in silico, thereby indicating enhanced clinical potential.

Abstract

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.

Problem

• drug discovery
• disease context
• molecular generation
• target protein
• generative model

Innovation

• disease-aware generative model
• target-conditioned molecular generation
• reinforcement learning with GRPO
• drug discovery
• GPT-2 fine-tuning