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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Ali Motahharynia, Mohammadreza Ghaffarzadeh-Esfahani, Mahsa Sheikholeslami, Navid Mazrouei, Matin Irajpour, Yousof Gheisari, Hajar Sirous
• Venue: arXiv: q-bio.QM
• Published: 2026-07-09
• Collected: 2026-07-10
• URL: http://arxiv.org/abs/2607.08404v1
• PDF: https://arxiv.org/pdf/2607.08404v1
• DOI: UNKNOWN

Plain-Language Brief

DrugGen 2 is a new model that generates small molecules for drug design by considering both disease ontology and target protein sequences. It was developed by fine-tuning a pre-trained GPT-2 model and outperformed other models in generating unique molecules, exhibiting greater structural similarity to approved drugs, and achieving improved predicted binding affinities for five protein targets related to diabetic nephropathy.

What the Document Says

• DrugGen-2 is a generative model for drug design that takes into account disease ontology and target protein sequences.
• It was developed by fine-tuning a pre-trained GPT-2 model.
• The development process involved supervised fine-tuning followed by reinforcement learning via group relative policy optimization (GRPO).
• The reward functions aimed for chemical validity, novelty, diversity, and high predicted binding affinity.
• When tested on five protein targets relevant to diabetic nephropathy, DrugGen-2 outperformed baseline models (DrugGPT and DrugGen).
• It showed a superior capacity to generate unique molecules.
• Its generated molecules exhibited greater structural similarity to approved drugs.
• It achieved improved predicted binding affinities across all targets.
• Molecular docking analyses supported these findings, identifying candidate ligands with strong binding potential.
• One of the compounds had a predicted affinity (-9.917) exceeding that of reference drugs like enalapril for angiotensin-converting enzyme (-8.283).

Key Concepts Mentioned

• DrugGen-2
• disease ontology
• target protein sequences
• GPT-2 model
• supervised fine-tuning
• reinforcement learning
• group relative policy optimization (GRPO)
• reward functions
• chemical validity
• novelty
• diversity
• predicted binding affinity
• diabetic nephropathy
• molecular docking

Claims Made by the Document

• DrugGen-2 outperforms baseline models.
• DrugGen-2 generates unique molecules.
• DrugGen-2's generated molecules exhibit greater structural similarity to approved drugs.
• DrugGen-2 achieves improved predicted binding affinities.

Evidence Used

• Superior capacity to generate unique molecules, exhibited greater structural similarity to approved drugs, and achieved improved predicted binding affinities across all targets.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.