Retro-fallback: retrosynthetic planning in an uncertain world
This addresses the practical challenge for chemists of creating reliable synthesis plans despite imperfect knowledge of reaction spaces, representing an incremental improvement over existing methods.
The paper tackles the problem of retrosynthetic planning under uncertainty about reaction feasibility, proposing a stochastic formulation and greedy algorithm called retro-fallback that maximizes the probability of successful lab execution. In-silico benchmarks show retro-fallback generally outperforms MCTS and retro* algorithms in producing better sets of synthesis plans.
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.