优化药物分子生成的噪声调度，提升几何结构与结合精度。
Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule
- 设计针对多模态分子结构的最优噪声调度策略
- 在CrossDock上达到95.9%的PoseBusters通过率，超基线10%以上
- 适合关注生成模型结构建模与药物设计的研究者
基于结构的药物设计（SBDD）对发现活性分子至关重要。当前深度生成模型在几何结构建模方面面临挑战，主要瓶颈在于连续3D坐标与离散2D拓扑联合决定分子几何时的复杂概率路径。我们发现噪声调度决定了该扭曲概率路径的变分下界（VLB），因此提出面向VLB最优的调度策略（VOS），将VLB作为路径积分目标用于SBDD。所提模型显著提升分子几何与相互作用建模能力，在交叉对接测试集上实现95.9%的PoseBusters通过率，较强基线提升超过10%，同时保持高亲和力与内在分子有效性。代码已开源：https://github.com/AlgoMole/MolCRAFT。
原文摘要 · Abstract (English)
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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