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

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.

@article{qiu2025_2505.07286,
  title={ Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule },
  author={ Keyue Qiu and Yuxuan Song and Zhehuan Fan and Peidong Liu and Zhe Zhang and Mingyue Zheng and Hao Zhou and Wei-Ying Ma },
  journal={arXiv preprint arXiv:2505.07286},
  year={ 2025 }
}