RIDE: Reference-Anchored Inference-Time Diffusion Editing for Scaffold Hopping· RIDE：基于参考的推理时扩散编辑实现骨架跃迁
Scaffold hopping is a critical task in drug discovery, which seeks to discover new, structurally distinct molecules that share key functional groups and similar 3D shape with a reference binding ligand. Existing diffusion-based scaffold hopping methods formulate the problem as conditional generation of scaffolds given the functional groups. However, they lack a principled mechanism to jointly enforce 2D structural novelty and preserve the 3D shape of the reference ligand. Here, we introduce RIDE, a Reference-anchored Inference-time Diffusion Editing framework for scaffold hopping. RIDE recovers the reference diffusion noise trajectory conditioned on the binding pocket and functional groups, selects an optimal trajectory segment for editing via noise perturbation, and conducts a value-guide
基于参考的扩散编辑技术实现药物发现中骨架跃迁的2D结构新颖性和3D形状保持。
- 核心方法
- RIDE框架首先重建参考扩散噪声轨迹，该轨迹考虑到结合口袋和功能团，然后通过噪声扰动选择最优轨迹段进行编辑，以指导生成过程。
- 适合谁读
- 药物研发领域研究者、AI模型开发工程师
- 要解决的问题
- 药物发现中的骨架跃迁任务需要生成与参考结合配体具有结构差异但功能团和3D形状相似的新分子。
- 关键实验
- 通过对比实验展示了RIDE在生成结构新颖且3D形状保持的分子方面的优越性；具体实验数据和案例请参见论文。
- 主要贡献
- 提出了一种新的扩散编辑框架RIDE，可以有效结合2D结构新颖性和3D形状保持，改进了现有的药物分子生成方法。
- 意义与局限
- RIDE为药物发现提供了更先进的分子生成技术，有助于加速新药研发过程，但在实际应用中可能需要更多的验证。