GenMol: A Drug Discovery Generalist with Discrete Diffusion
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915405294993408 |
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| author | Lee, Seul Kreis, Karsten Veccham, Srimukh Prasad Liu, Meng Reidenbach, Danny Peng, Yuxing Paliwal, Saee Nie, Weili Vahdat, Arash |
| author_facet | Lee, Seul Kreis, Karsten Veccham, Srimukh Prasad Liu, Meng Reidenbach, Danny Peng, Yuxing Paliwal, Saee Nie, Weili Vahdat, Arash |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GenMol: A Drug Discovery Generalist with Discrete Diffusion Lee, Seul Kreis, Karsten Veccham, Srimukh Prasad Liu, Meng Reidenbach, Danny Peng, Yuxing Paliwal, Saee Nie, Weili Vahdat, Arash Machine Learning 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. |
| title | GenMol: A Drug Discovery Generalist with Discrete Diffusion |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.06158 |