@inproceedings{tripp2023retro-fallback,
author = {Tripp, Austin and Maziarz, Krzysztof and Lewis, Sarah and Hernandez-Lobato, Jose Miguel and Segler, Marwin},
title = {Retro-fallback: retrosynthetic planning in an uncertain world},
booktitle = {ICLR 2024},
year = {2023},
month = {October},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/retro-fallback-retrosynthetic-planning-in-an-uncertain-world/},
}