用拓扑引导生成大环分子，效率提升近百倍
MacroGuide: Topological Guidance for Macrocycle Generation
- 基于持久同调分析原子结构，动态引导扩散模型生成环状分子
- 在无条件与靶点条件设置下，大环生成率从1%提升至99%
- 兼顾化学合理性、多样性，适合药物分子生成研究者
大环分子因其对难靶点更强的选择性和结合力，是小分子药物的潜在替代品。然而，由于公开数据集中的稀缺性及标准生成模型难以满足拓扑约束，其在生成建模中仍被忽视。我们提出MacroGuide：一种基于持久同调的拓扑引导机制，用于指导预训练分子生成扩散模型在无条件与条件（蛋白口袋）设置下生成大环分子。在每个去噪步骤中，MacroGuide基于原子位置构建维托里斯-里普斯复形，并通过优化持久同调特征促进环状结构形成。实证表明，应用MacroGuide后，大环生成率从1%提升至99%，同时在化学有效性、多样性和PoseBusters检测等关键指标上达到或超越当前最优水平。
原文摘要 · Abstract (English)
Macrocycles are ring-shaped molecules that offer a promising alternative to small-molecule drugs due to their enhanced selectivity and binding affinity against difficult targets. Despite their chemical value, they remain underexplored in generative modeling, likely owing to their scarcity in public datasets and the challenges of enforcing topological constraints in standard deep generative models. We introduce MacroGuide: Topological Guidance for Macrocycle Generation, a diffusion guidance mechanism that uses Persistent Homology to steer the sampling of pretrained molecular generative models toward the generation of macrocycles, in both unconditional and conditional (protein pocket) settings. At each denoising step, MacroGuide constructs a Vietoris-Rips complex from atomic positions and promotes ring formation by optimizing persistent homology features. Empirically, applying MacroGuide to pretrained diffusion models increases macrocycle generation rates from 1% to 99%, while matching or exceeding state-of-the-art performance on key quality metrics such as chemical validity, diversity, and PoseBusters checks.
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