Skip to yearly menu bar Skip to main content


Retro-fallback: retrosynthetic planning in an uncertain world

Austin Tripp · Krzysztof Maziarz · Sarah Lewis · Marwin Segler · José Miguel Hernández Lobato

Halle B #7
[ ] [ Project Page ]
Wed 8 May 7:30 a.m. PDT — 9:30 a.m. PDT


Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules. While previous works have proposed algorithms to find optimal solutions for a range of metrics (e.g. shortest, lowest-cost), these works generally overlook the fact that we have imperfect knowledge of the space of possible reactions, meaning plans created by algorithms may not work in a laboratory. In this paper we propose a novel formulation of retrosynthesis in terms of stochastic processes to account for this uncertainty. We then propose a novel greedy algorithm called retro-fallback which maximizes the probability that at least one synthesis plan can be executed in the lab. Using in-silico benchmarks we demonstrate that retro-fallback generally produces better sets of synthesis plans than the popular MCTS and retro* algorithms.

Chat is not available.