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.</p>\n","updatedAt":"2026-07-10T08:37:42.983Z","author":{"_id":"61990d48d7f09e0d8b7714de","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/61990d48d7f09e0d8b7714de/sL3Tc36iuBZr0_Dy0wWCc.jpeg","fullname":"Ali Motahharynia","name":"alimotahharynia","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9050942063331604},"editors":["alimotahharynia"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/61990d48d7f09e0d8b7714de/sL3Tc36iuBZr0_Dy0wWCc.jpeg"],"reactions":[{"reaction":"😎","users":["alifardinia00"],"count":1}],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2607.08404","authors":[{"_id":"6a50a88375fd3d966bd45f0f","name":"Ali Motahharynia","hidden":false},{"_id":"6a50a88375fd3d966bd45f10","name":"Mohammadreza Ghaffarzadeh-Esfahani","hidden":false},{"_id":"6a50a88375fd3d966bd45f11","name":"Mahsa Sheikholeslami","hidden":false},{"_id":"6a50a88375fd3d966bd45f12","name":"Navid Mazrouei","hidden":false},{"_id":"6a50a88375fd3d966bd45f13","name":"Matin Irajpour","hidden":false},{"_id":"6a50a88375fd3d966bd45f14","name":"Yousof Gheisari","hidden":false},{"_id":"6a50a88375fd3d966bd45f15","name":"Hajar Sirous","hidden":false}],"publishedAt":"2026-07-09T00:00:00.000Z","submittedOnDailyAt":"2026-07-10T00:00:00.000Z","title":"DrugGen 2: A disease-aware language model for enhancing drug discovery","submittedOnDailyBy":{"_id":"61990d48d7f09e0d8b7714de","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/61990d48d7f09e0d8b7714de/sL3Tc36iuBZr0_Dy0wWCc.jpeg","isPro":false,"fullname":"Ali Motahharynia","user":"alimotahharynia","type":"user","name":"alimotahharynia"},"summary":"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.","upvotes":12,"discussionId":"6a50a88475fd3d966bd45f16","projectPage":"https://huggingface.co/spaces/alimotahharynia/DrugGen-2","githubRepo":"https://github.com/alimotahharynia/DrugGen-2","githubRepoAddedBy":"user","ai_summary":"DrugGen-2 generates small molecules conditioned on disease ontology and target protein sequences through fine-tuning GPT-2 with supervised learning and reinforcement learning using GRPO, achieving superior molecular diversity and binding affinity compared to baseline models.","ai_keywords":["GPT-2","supervised fine-tuning","reinforcement learning","group relative policy optimization","chemical validity","molecular generation","binding affinity","molecular docking"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","githubStars":3},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"61990d48d7f09e0d8b7714de","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/61990d48d7f09e0d8b7714de/sL3Tc36iuBZr0_Dy0wWCc.jpeg","isPro":false,"fullname":"Ali Motahharynia","user":"alimotahharynia","type":"user"},{"_id":"645d63c0ce72244df7b36be8","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/645d63c0ce72244df7b36be8/09vhYAzgv1svwvQM4eIE9.jpeg","isPro":false,"fullname":"MoRezaGH","user":"Moreza009","type":"user"},{"_id":"6a2da6c8ca070ee12c6e396c","avatarUrl":"/avatars/0355287dcabaa67dbc7f0b10b87451f9.svg","isPro":false,"fullname":"Joe Mama","user":"JoeMama123123123","type":"user"},{"_id":"6434b6619bd5a84b5dcfa4de","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6434b6619bd5a84b5dcfa4de/h8Q6kPNjFNc03wmdboHzq.jpeg","isPro":true,"fullname":"Young-Jun Lee","user":"passing2961","type":"user"},{"_id":"6789b4147bd5f0f1891d0525","avatarUrl":"/avatars/74c1fedf42bf3718eb88dcaf1a762f2a.svg","isPro":false,"fullname":"Nahid Yousefian","user":"nahidysf","type":"user"},{"_id":"67c40fa2c0a504dc033f6c8b","avatarUrl":"/avatars/a2a92b7bbc1d875687198bd18d2f9303.svg","isPro":false,"fullname":"hossein bekas","user":"hossein12365555555","type":"user"},{"_id":"67c412b1ecc2b3bb52331880","avatarUrl":"/avatars/7f6ebaec60f6f42753bb351efa74902e.svg","isPro":false,"fullname":"ali kobraiian","user":"kobraiian898989","type":"user"},{"_id":"651fce4ffc791e23f7208490","avatarUrl":"/avatars/d0967380dc3dbc7012f16b64a4a1a6f4.svg","isPro":false,"fullname":"Michel Kafold","user":"Michel-kafold009","type":"user"},{"_id":"671695e68c364caa43429de8","avatarUrl":"/avatars/0397a28b80ae0996c96677a7956e301f.svg","isPro":false,"fullname":"maxinose","user":"ahmadmax77","type":"user"},{"_id":"6716addaf51dd4be6650962c","avatarUrl":"/avatars/0c10d10407c45983cb470b289b33de80.svg","isPro":false,"fullname":"Sorour ragheb","user":"Sorour122","type":"user"},{"_id":"67181b533a5fdf2aeefdba6f","avatarUrl":"/avatars/52641a256152c64293c4e64e77e21ad6.svg","isPro":false,"fullname":"sual","user":"bettercall96","type":"user"},{"_id":"651e865843e7d4ac931b8519","avatarUrl":"/avatars/72833bf2f77842b6ca880453dfe67784.svg","isPro":false,"fullname":"ali fardini","user":"alifardinia00","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2607/2607.08404.md","query":{}}">
DrugGen 2: A disease-aware language model for enhancing drug discovery
Abstract
DrugGen-2 generates small molecules conditioned on disease ontology and target protein sequences through fine-tuning GPT-2 with supervised learning and reinforcement learning using GRPO, achieving superior molecular diversity and binding affinity compared to baseline models.
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.
Community
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.
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