Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916780196233216 |
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| author | Qiu, Keyue Song, Yuxuan Fan, Zhehuan Liu, Peidong Zhang, Zhe Zheng, Mingyue Zhou, Hao Ma, Wei-Ying |
| author_facet | Qiu, Keyue Song, Yuxuan Fan, Zhehuan Liu, Peidong Zhang, Zhe Zheng, Mingyue Zhou, Hao Ma, Wei-Ying |
| contents | Structure-Based Drug Design (SBDD) is crucial for identifying bioactive molecules. Recent deep generative models are faced with challenges in geometric structure modeling. A major bottleneck lies in the twisted probability path of multi-modalities -- continuous 3D positions and discrete 2D topologies -- which jointly determine molecular geometries. By establishing the fact that noise schedules decide the Variational Lower Bound (VLB) for the twisted probability path, we propose VLB-Optimal Scheduling (VOS) strategy in this under-explored area, which optimizes VLB as a path integral for SBDD. Our model effectively enhances molecular geometries and interaction modeling, achieving state-of-the-art PoseBusters passing rate of 95.9% on CrossDock, more than 10% improvement upon strong baselines, while maintaining high affinities and robust intramolecular validity evaluated on held-out test set. Code is available at https://github.com/AlgoMole/MolCRAFT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07286 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule Qiu, Keyue Song, Yuxuan Fan, Zhehuan Liu, Peidong Zhang, Zhe Zheng, Mingyue Zhou, Hao Ma, Wei-Ying Biomolecules Artificial Intelligence Machine Learning Structure-Based Drug Design (SBDD) is crucial for identifying bioactive molecules. Recent deep generative models are faced with challenges in geometric structure modeling. A major bottleneck lies in the twisted probability path of multi-modalities -- continuous 3D positions and discrete 2D topologies -- which jointly determine molecular geometries. By establishing the fact that noise schedules decide the Variational Lower Bound (VLB) for the twisted probability path, we propose VLB-Optimal Scheduling (VOS) strategy in this under-explored area, which optimizes VLB as a path integral for SBDD. Our model effectively enhances molecular geometries and interaction modeling, achieving state-of-the-art PoseBusters passing rate of 95.9% on CrossDock, more than 10% improvement upon strong baselines, while maintaining high affinities and robust intramolecular validity evaluated on held-out test set. Code is available at https://github.com/AlgoMole/MolCRAFT. |
| title | Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule |
| topic | Biomolecules Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.07286 |