Retro-fallback: retrosynthetic planning in an uncertain world

• URL: http://arxiv.org/abs/2310.09270v3
• Date: Sun, 14 Apr 2024 02:50:35 GMT
• Title: Retro-fallback: retrosynthetic planning in an uncertain world
• Authors: Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin Segler, José Miguel Hernández-Lobato,
• Abstract summary: Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules. We propose a novel formulation of retrosynthesis in terms of processes to account for this uncertainty. We then propose a novel greedy algorithm called retro-fallback which maximizes the probability that at least one plan can be executed in the lab.
• Score: 29.35379180648418
• License: http://creativecommons.org/licenses/by/4.0/
• 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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