@inproceedings{
tripp2023retrofallback,
title={Retro-fallback: retrosynthetic planning in an uncertain world},
author={Austin Tripp and Krzysztof Maziarz and Sarah Lewis and Marwin Segler and Jos{\'e} Miguel Hern{\'a}ndez-Lobato},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=oRP132De46}
}
Retrosynthesis is the task of proposing 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 the algorithm 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. We encourage the reader to view the full version of this paper at https://arxiv.org/abs/2310.09270.
Retro-fallback: retrosynthetic planning in an uncertain world
Austin Tripp Krzysztof Maziarz Sarah Lewis Marwin Segler José Miguel Hernández-Lobato