Systems-Structure-Based Drug Design
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913544668184576 |
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| author | Zaballa, Vincent D. Hui, Elliot E. |
| author_facet | Zaballa, Vincent D. Hui, Elliot E. |
| contents | Recent advances in generative deep learning have transformed small molecule design, but most methods lack biological systems context, focusing narrowly on specific protein pockets. We introduce a non-differentiable diffusion guidance method that integrates systems biology models, enhancing small molecule generation with pathway context. Using the Bone Morphogenetic Protein (BMP) pathway, we generate small molecules specific to one protein over competing proteins. This method enhances the precision and efficiency of small molecule drug discovery by incorporating systems biology insights into generative models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10108 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Systems-Structure-Based Drug Design Zaballa, Vincent D. Hui, Elliot E. Biomolecules Recent advances in generative deep learning have transformed small molecule design, but most methods lack biological systems context, focusing narrowly on specific protein pockets. We introduce a non-differentiable diffusion guidance method that integrates systems biology models, enhancing small molecule generation with pathway context. Using the Bone Morphogenetic Protein (BMP) pathway, we generate small molecules specific to one protein over competing proteins. This method enhances the precision and efficiency of small molecule drug discovery by incorporating systems biology insights into generative models. |
| title | Systems-Structure-Based Drug Design |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2410.10108 |