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

Track: Machine learning: computational method and/or computational results

Nature Biotechnology: No

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× improvement in sampling efficiency compared to 2D synthesis-based baseline. To our best knowledge, our method is also the first to achieve best performance in both Vina Dock (-9.38 kcal/mol) and AiZynthFinder success rate (62.2%) on the CrossDocked benchmark.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Tony_Shen1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 18