Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915389649190912 |
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| author | Shen, Tony Seo, Seonghwan Irwin, Ross Didi, Kieran Olsson, Simon Kim, Woo Youn Ester, Martin |
| author_facet | Shen, Tony Seo, Seonghwan Irwin, Ross Didi, Kieran Olsson, Simon Kim, Woo Youn Ester, Martin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Compositional Flows for 3D Molecule and Synthesis Pathway Co-design Shen, Tony Seo, Seonghwan Irwin, Ross Didi, Kieran Olsson, Simon Kim, Woo Youn Ester, Martin Machine Learning Artificial Intelligence 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. |
| title | Compositional Flows for 3D Molecule and Synthesis Pathway Co-design |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08051 |