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2. GenMol: A Drug Discovery Generalist with Discrete Diffusion
 
 # GenMol: A Drug Discovery Generalist with Discrete Diffusion

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 Drug discovery is a complex process that involves multiple stages and tasks. However, existing molecular generative models can only tackle some of these tasks. We present Generalist Molecular generative model (GenMol), a versatile framework that uses only a single discrete diffusion model to handle diverse drug discovery scenarios. GenMol generates Sequential Attachment-based Fragment Embedding (SAFE) sequences through non-autoregressive bidirectional parallel decoding, thereby allowing the utilization of a molecular context that does not rely on the specific token ordering while having better sampling efficiency. GenMol uses fragments as basic building blocks for molecules and introduces fragment remasking, a strategy that optimizes molecules by regenerating masked fragments, enabling effective exploration of chemical space. We further propose molecular context guidance (MCG), a guidance method tailored for masked discrete diffusion of GenMol. GenMol significantly outperforms the previous GPT-based model in de novo generation and fragment-constrained generation, and achieves state-of-the-art performance in goal-directed hit generation and lead optimization. These results demonstrate that GenMol can tackle a wide range of drug discovery tasks, providing a unified and versatile approach for molecular design. Our code is available at <https://github.com/NVIDIA-Digital-Bio/genmol>.



 ## Authors



Seul Lee (KAIST)

[Karsten Kreis](/index.php/person/karsten-kreis)

Srimukh Prasad Veccham (NVIDIA)

Meng Liu (NVIDIA)

Danny Reidenbach (NVIDIA)

Yuxing Peng (NVIDIA)

Saee Paliwal (NVIDIA)

Weili Nie (NVIDIA)

[Arash Vahdat](/index.php/person/arash-vahdat)

 

 

 ## Publication Date



Sunday, July 13, 2025

 

 ## Published in



[International Conference on Machine Learning (ICML) 2025](https://arxiv.org/abs/2501.06158)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence)

[Generative AI](/index.php/research-area/generative-ai)