GenMol: A Drug Discovery Generalist with Discrete Diffusion

This is the official code repository for the paper titled GenMol: A Drug Discovery Generalist with Discrete Diffusion (ICML 2025).

Contribution

We introduce GenMol, a model for unified and versatile molecule generation by building masked discrete diffusion that generates SAFE molecular sequences.
We propose fragment remasking, an effective strategy for exploring chemical space using molecular fragments as the unit of exploration.
We propose molecular context guidance (MCG), a guidance scheme for GenMol to effectively utilize molecular context information.
We validate the efficacy and versatility of GenMol on a wide range of drug discovery tasks.

License

Copyright @ 2025, NVIDIA Corporation. All rights reserved.
\nThe source code is made available under Apache-2.0.
\nThe model weights are made available under the NVIDIA Open Model License.

Citation

If you find this repository and our paper useful, we kindly request to cite our work.

@article{lee2025genmol,\n  title     = {GenMol: A Drug Discovery Generalist with Discrete Diffusion},\n  author    = {Lee, Seul and Kreis, Karsten and Veccham, Srimukh Prasad and Liu, Meng and Reidenbach, Danny and Peng, Yuxing and Paliwal, Saee and Nie, Weili and Vahdat, Arash},\n  journal   = {International Conference on Machine Learning},\n  year      = {2025}\n}\n

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GenMol: A Drug Discovery Generalist with Discrete Diffusion

\n\nThis is the official code repository for the paper titled [GenMol: A Drug Discovery Generalist with Discrete Diffusion](https://arxiv.org/abs/2501.06158) (ICML 2025).\n\n\n## Contribution\n+ We introduce GenMol, a model for unified and versatile molecule generation by building masked discrete diffusion that generates SAFE molecular sequences.\n+ We propose fragment remasking, an effective strategy for exploring chemical space using molecular fragments as the unit of exploration.\n+ We propose molecular context guidance (MCG), a guidance scheme for GenMol to effectively utilize molecular context information.\n+ We validate the efficacy and versatility of GenMol on a wide range of drug discovery tasks.\n\n## License\nCopyright @ 2025, NVIDIA Corporation. All rights reserved.
\nThe source code is made available under Apache-2.0.
\nThe model weights are made available under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n## Citation\nIf you find this repository and our paper useful, we kindly request to cite our work.\n```BibTex\n@article{lee2025genmol,\n title = {GenMol: A Drug Discovery Generalist with Discrete Diffusion},\n author = {Lee, Seul and Kreis, Karsten and Veccham, Srimukh Prasad and Liu, Meng and Reidenbach, Danny and Peng, Yuxing and Paliwal, Saee and Nie, Weili and Vahdat, Arash},\n journal = {International Conference on Machine Learning},\n year = {2025}\n}\n```","GenMol",["D",1752711846296],"NVIDIA BioNeMo","/api/image/nvidia/clara/resources/genmol_v1","bias","explainability","privacy","safetyAndSecurity",[],[258,259,260,261],[],[],[],{"_90":270,"_120":271,"_121":272,"_123":124},{"_90":266,"_120":267,"_121":268,"_123":124},{"_90":263,"_120":263,"_121":264,"_123":124},{"_90":119,"_120":119,"_121":262,"_123":124},{"_125":124,"_120":119},"Model",{"_125":265,"_120":263},"type","Healthcare","indus_healthcare",{"_125":269,"_120":267},"industry","Drug Discovery","uscs_drug_discovery",{"_125":273,"_120":271},"application",{"_152":1248,"_154":1249},"ngcApps",[1201],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1182":1183,"_1184":1185,"_1186":1187,"_1188":1189,"_1190":285,"_183":311,"_1191":1192,"_1193":51,"_1194":131,"_1195":131,"_1196":1197,"_1198":131,"_1199":1200},"amplitude",{"_1179":1180,"_823":-7,"_1181":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1173":1174,"_1175":1176,"_1177":1178},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788511853197,"1.15.0","defaultSearchSuggestions",{"_1063":94,"_1064":1065,"_1066":1067,"_1068":1069},"announcements",[],"consentState",{"_349":1053,"_1054":131,"_1055":26,"_1056":26,"_1057":1058},"consentConfig",{"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350},"showContactAdmin",1788511853199,[],[],[325,326,327,328,329,330],{"_40":55,"_42":64,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":61,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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