[00:00] Introduction to SynFlowNet

[00:29] Problem: AI-generated molecules often can't be synthesized

[01:17] Solution: SynFlowNet - uses real chemical reactions

[02:03] GFlowNets: Enables diverse molecule generation

[02:47] Scalability: Morgan fingerprints handle 200K+ compounds

[03:14] Challenge: Solving backward trajectory issues

[04:14] Results: Better synthesis rates and molecular diversity

[05:30] Scale test: Successfully handled 221K molecules

[06:06] Application: Integration with fragment screening

[06:38] Wrap-up: SynFlowNet advances drug design

Authors: Miruna Cretu, Charles Harris, Ilia Igashov, Arne Schneuing, Marwin Segler, Bruno Correia, Julien Roy, Emmanuel Bengio, Pietro Liò

Affiliations: University of Cambridge, EPFL, Microsoft Research, Valence Labs

Abstract: Generative models see increasing use in computer-aided drug design. However, while performing well at capturing distributions of molecular motifs, they often produce synthetically inaccessible molecules. To address this, we introduce SynFlowNet, a GFlowNet model whose action space uses chemical reactions and buyable reactants to sequentially build new molecules. By incorporating forward synthesis as an explicit constraint of the generative mechanism, we aim at bridging the gap between in silico molecular generation and real world synthesis capabilities. We evaluate our approach using synthetic accessibility scores and an independent retrosynthesis tool to assess the synthesizability of our compounds, and motivate the choice of GFlowNets through considerable improvement in sample diversity compared to baselines. Additionally, we identify challenges with reaction encodings that can complicate traversal of the MDP in the backward direction. To address this, we introduce various strategies for learning the GFlowNet backward policy and thus demonstrate how additional constraints can be integrated into the GFlowNet MDP framework. This approach enables our model to successfully identify synthesis pathways for previously unseen molecules.

Link: https://arxiv.org/abs/2405.01155v2