MacroGuide：宏环生成的拓扑引导
MacroGuide: Topological Guidance for Macrocycle Generation
AI总结：
MacroGuide通过持久同调引导预训练扩散模型生成宏环，显著提高生成效率并提升质量指标
摘要：
宏环是环状分子，因其在难靶上的增强选择性和结合亲和力而成为小分子药物的有希望替代品。尽管它们具有化学价值，但在生成建模中仍处于未探索状态，这可能是由于公共数据集中宏环稀缺以及在标准深度生成模型中强制执行拓扑约束的挑战。我们引入了MacroGuide：宏环生成的拓扑引导，这是一种扩散引导机制，利用持久同调来引导预训练分子生成模型的采样，以生成宏环，无论是无条件还是条件（蛋白质口袋）设置。在每个去噪步骤中，MacroGuide从原子位置构建Vietoris-Rips复形，并通过优化持久同调特征来促进环的形成。实证上，将MacroGuide应用于预训练扩散模型，使宏环生成率从1%提升到99%，同时在化学有效性、多样性和PoseBusters检查等关键质量指标上匹配或超过最先进的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
