Steerable Single-step Retrosynthesis with Electron-pushing Discrete Flow Matching and Feynman-Kac Steering

Abstract

Expert chemists perform retrosynthetic analysis in a strategic manner, where each reaction in a route is employed with a specific purpose, combined with mechanistic reasoning represented as electron-pushing arrows to visualize how bonds are formed or broken via electron redistribution. Yet contemporary single-step retrosynthesis models exhibit neither of these properties: their learned reaction distribution is fixed to that of the training data, and reactions are predicted via de-novo generation of the precursors or molecular graph edits, omitting the electron redistribution that governs the chemical transformations. We introduce Verso MAELLE, the first electron-pushing-based single-step retrosynthetic model, representing disconnections as continuous-time Markov chains (CTMC) comprised of edits in the electron space, and is trainable using a discrete flow matching formulation. Being a generative model, Verso MAELLE is optionally steerable at inference time through the Feynman-Kac (FK) framework, using rewards that promote certain types of disconnections. We show that besides in-distribution learning, Verso MAELLE can be steered toward cyclization reactions which are scaffold-constructive but underrepresented in the training corpus, via a ring-breaking reward. As a proof of concept, we show how the electron-pushing reasoning, learned from patented data, combined with a domino incentivizing reward, could steer Verso MAELLE toward creative and strategic disconnections, demonstrated on the key domino sequence that was used to synthesize Punctaporonin U, a complex natural product of the sesquiterpene family.