@inproceedings{
yu2024doubleended,
title={Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search},
author={Kevin Yu and Jihye Roh and Ziang Li and Wenhao Gao and Runzhong Wang and Connor W. Coley},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=LJNqVIKSCr}
}
Computer-aided synthesis planning (CASP) algorithms have demonstrated expert-level abilities in planning retrosynthetic routes to molecules of low to moderate complexity. However, current search methods assume the sufficiency of reaching arbitrary building blocks, failing to address the common real-world constraint where using specific molecules is desired. To this end, we present a formulation of synthesis planning with starting material constraints. Under this formulation, we propose Double-Ended Synthesis Planning ($\texttt{DESP}$), a novel CASP algorithm under a _bidirectional graph search_ scheme that interleaves expansions from the target and from the goal starting materials to ensure constraint satisfiability. The search algorithm is guided by a goal-conditioned cost network learned offline from a partially observed hypergraph of valid chemical reactions. We demonstrate the utility of $\texttt{DESP}$ in improving solve rates and reducing the number of search expansions by biasing synthesis planning towards expert goals on multiple new benchmarks. $\texttt{DESP}$ can make use of existing one-step retrosynthesis models, and we anticipate its performance to scale as these one-step model capabilities improve.
Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search
Kevin Yu Jihye Roh Ziang Li Wenhao Gao Runzhong Wang Connor W. Coley