Compositional Flows for 3D Molecule and Synthesis Pathway Co-design

• URL: http://arxiv.org/abs/2504.08051v1
• Date: Thu, 10 Apr 2025 18:10:34 GMT
• Title: Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
• Authors: Tony Shen, Seonghwan Seo, Ross Irwin, Kieran Didi, Simon Olsson, Woo Youn Kim, Martin Ester,
• Abstract summary: We introduce Compositional Generative Flows (CGFlow), a novel framework that extends flow matching to generate objects in compositional steps.<n>We build upon the theoretical foundations of generative flow networks (GFlowNets), enabling reward-guided sampling of compositional structures.<n>We apply CGFlow to synthesizable drug design by jointly designing the molecule's synthetic pathway with its 3D binding pose.
• Score: 3.7359205703290024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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