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.
The source code is made available under Apache-2.0.
The 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,
  title     = {GenMol: A Drug Discovery Generalist with Discrete Diffusion},
  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},
  journal   = {International Conference on Machine Learning},
  year      = {2025}
}