发表机构
The Ohio State University（俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RIDE是一种参考锚定的推理时扩散编辑框架，通过恢复参考噪声轨迹并选择最优段进行编辑，实现骨架跃迁，在保持高三维相似度的同时降低二维相似度，平均改进11.7%和7.3%。
AI 中文摘要
骨架跃迁是药物发现中的一项关键任务，旨在发现与参考结合配体共享关键官能团和相似三维形状的、结构上不同的新分子。现有的基于扩散的骨架跃迁方法将问题表述为在给定官能团的条件下生成骨架。然而，它们缺乏一种有原则的机制来同时强制二维结构新颖性并保持参考配体的三维形状。在此，我们提出了RIDE，一种用于骨架跃迁的参考锚定推理时扩散编辑框架。RIDE恢复以结合口袋和官能团为条件的参考扩散噪声轨迹，通过噪声扰动选择用于编辑的最优轨迹段，并进行价值引导的骨架采样以生成新骨架。大量实验结果表明，与基线相比，RIDE始终生成与参考相比二维相似度更低、三维相似度更高的骨架，平均改进分别为11.7%和7.3%。进一步分析表明，RIDE可以适应各种奖励函数，并且即使三维相似度未明确包含在奖励中，也能保持三维相似度。两个案例研究展示了RIDE生成具有不同结构和性质的不同骨架的能力，以及其在保持非常高的三维相似度的同时引入大量二维变化的能力。RIDE可在以下网址公开获取：此https URL。
英文摘要
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-guided scaffold sampling to generate new scaffolds. Extensive experimental results demonstrate that, compared to baselines, RIDE consistently generates scaffolds with lower 2D similarity and higher 3D similarity to the reference, with an average improvements of 11.7% and 7.3%, respectively. Further analysis reveals that RIDE can accommodate various reward functions, and can preserve 3D similarity even when this is not explicitly included in the reward. Two case studies illustrate RIDE's ability to generate distinct scaffolds with different structures and properties, and its ability to introduce substantial 2D variation while maintaining very high 3D similarity. RIDE is publicly available at https://anonymous.4open.science/r/RIDE-C8A0.
Comments20 pages, 6 figures
