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

Tony Shen, Seonghwan Seo, Ross Irwin, Kieran Didi, Simon Olsson, Woo Youn Kim, Martin Ester

Submission Track: Full Paper

Submission Category: AI-Guided Design

Keywords: drug discovery, synthesizable molecular design, GFlowNets, flow matching

TL;DR: GFlowNets meet flow matching for 3D molecules generation via synthesis pathways.

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.

Submission Number: 46