用流匹配生成环状分子构象，确保结构合法且多样精确。
Generating Cyclic Conformers with Flow Matching in Cremer-Pople Coordinates
- 在Cremer-Pople坐标系中做流匹配，直接生成有效闭合环
- 生成的构象在多样性与精度上均优于现有方法
- 适合药物和催化领域环状分子的高效生成
环状分子在化学与生物学中广泛存在，其受限的构象灵活性提供了关键的结构预组织，对药物发现和催化至关重要。然而，可靠采样环系构象集仍具挑战。本文提出PuckerFlow，一种基于流匹配的生成模型，在低维内部坐标系Cremer-Pople空间中进行建模，可直接生成有效闭合环。该方法在生成构象的多样性与精确性方面表现优异，几乎在所有定量指标上超越现有方法，并展示了在催化与药物发现相关环系中的应用潜力。本工作实现了环状结构的高效可靠构象生成，为建模结构-性质关系及属性引导的环状分子生成奠定了基础。
原文摘要 · Abstract (English)
Cyclic molecules are ubiquitous across applications in chemistry and biology. Their restricted conformational flexibility provides structural pre-organization that is key to their function in drug discovery and catalysis. However, reliably sampling the conformer ensembles of ring systems remains challenging. Here, we introduce PuckerFlow, a generative machine learning model that performs flow matching on the Cremer-Pople space, a low-dimensional internal coordinate system capturing the relevant degrees of freedom of rings. Our approach enables generation of valid closed rings by design and demonstrates strong performance in generating conformers that are both diverse and precise. We show that PuckerFlow outperforms other conformer generation methods on nearly all quantitative metrics and illustrate the potential of PuckerFlow for ring systems relevant to chemical applications, particularly in catalysis and drug discovery. This work enables efficient and reliable conformer generation of cyclic structures, paving the way towards modeling structure-property relationships and the property-guided generation of rings across a wide range of applications in chemistry and biology.
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