同时生成分子结构与合成路径，提升药物设计效率与精度
Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
- 分步生成分子与合成路径，结合连续状态建模
- 15个靶点上均达当前最优结合亲和力，采样效率提升5.8倍
- 首次在交叉对接数据集上同时突破虚拟筛选与合成成功率
许多生成应用，如基于合成的3D分子设计，涉及具有连续特征的组合对象构建。本文提出组合生成流（CGFlow），将流匹配扩展至分步生成组合对象并建模连续状态。核心思想是将组合状态转移建模为流匹配插值过程的直接延伸。我们进一步基于生成流网络（GFlowNets）的理论基础，实现奖励引导的组合结构采样。将CGFlow应用于可合成药物设计，联合优化分子的合成路径与三维结合构象。在LIT-PCBA基准的全部15个靶点上均达到当前最优结合亲和力，采样效率较2D合成基线提升5.8倍。据我们所知，该方法也是首个在CrossDocked基准上同时达到Vina打分-9.38和AiZynth合成成功率62.2%的模型。
原文摘要 · Abstract (English)
Many generative applications, such as synthesis-based 3D molecular design, involve constructing compositional objects with continuous features. Here, we introduce Compositional Generative Flows (CGFlow), a novel framework that extends flow matching to generate objects in compositional steps while modeling continuous states. Our key insight is that modeling compositional state transitions can be formulated as a straightforward extension of the flow matching interpolation process. We further build upon the theoretical foundations of generative flow networks (GFlowNets), enabling reward-guided sampling of compositional structures. We apply CGFlow to synthesizable drug design by jointly designing the molecule's synthetic pathway with its 3D binding pose. Our approach achieves state-of-the-art binding affinity on all 15 targets from the LIT-PCBA benchmark, and 5.8$\times$ improvement in sampling efficiency compared to 2D synthesis-based baseline. To our best knowledge, our method is also the first to achieve state of-art-performance in both Vina Dock (-9.38) and AiZynth success rate (62.2\%) on the CrossDocked benchmark.
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