Retro-fallback: retrosynthetic planning in an uncertain world

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

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Keywords: Retrosynthesis, planning, chemistry, search

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TL;DR: We propose an algorithm to produce multiple retrosynthesis plans whose real-life outcomes are uncorrelated

Abstract: 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.

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Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 2450