Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule

• URL: http://arxiv.org/abs/2505.07286v2
• Date: Thu, 05 Jun 2025 12:37:46 GMT
• Title: Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule
• Authors: Keyue Qiu, Yuxuan Song, Zhehuan Fan, Peidong Liu, Zhe Zhang, Mingyue Zheng, Hao Zhou, Wei-Ying Ma,
• Abstract summary: Recent deep generative models are faced with challenges in geometric structure modeling.<n>By establishing the fact that noise schedules decide the Variational Lower Bound (VLB) for the twisted probability path, we propose VLB- Optimal Scheduling (VOS)<n>Our model effectively enhances molecular geometries and interaction modeling, achieving state-of-the-art PoseBusters passing rate of 95.9% on CrossDock.
• Score: 19.871512049860076
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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