FLOWR通过流匹配生成三维配体，速度快且能精准设计特定相互作用。
FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation
- 用连续与类别流匹配+等变最优传输生成配体
- 比现有方法快70倍，交互和构象准确率更高
- 可按需求生成带特定化学结构的片段分子
我们提出FLOWR，一种基于结构的新一代三维配体生成与优化框架。它结合连续与类别流匹配、等变最优传输，并通过高效蛋白口袋条件化增强。同时，我们构建了SPINDR数据集，包含经严格筛选的配体-口袋共晶复合物，以解决现有数据质量问题。实验表明，FLOWR在PoseBusters有效性、构象精度和相互作用恢复方面优于当前最先进的扩散与流模型，推理速度提升高达70倍。此外，我们推出FLOWR:multi，一个高精度多功能模型，可在无需重训练或重采样的情况下，针对预设的相互作用模式和化学骨架进行靶向采样，适用于片段式药物设计。
原文摘要 · Abstract (English)
We introduce FLOWR, a novel structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and categorical flow matching with equivariant optimal transport, enhanced by an efficient protein pocket conditioning. Alongside FLOWR, we present SPINDR, a thoroughly curated dataset comprising ligand-pocket co-crystal complexes specifically designed to address existing data quality issues. Empirical evaluations demonstrate that FLOWR surpasses current state-of-the-art diffusion- and flow-based methods in terms of PoseBusters-validity, pose accuracy, and interaction recovery, while offering a significant inference speedup, achieving up to 70-fold faster performance. In addition, we introduce FLOWR:multi, a highly accurate multi-purpose model allowing for the targeted sampling of novel ligands that adhere to predefined interaction profiles and chemical substructures for fragment-based design without the need of re-training or any re-sampling strategies
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