Higher-level Strategies for Computer-Aided Retrosynthesis

20 February 2025, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Retrosynthesis is a core technique in organic chemistry that involves simplifying target molecules into more readily available components. Computer-aided synthesis planning (CASP) tools automate this process by recursively proposing immediate precursors of molecules to identify multistep synthetic pathways. However, the application of CASP tools becomes particularly challenging for complex molecules due to the increased length of synthetic pathways and the increased number of possible disconnections. We introduce a novel framework for computer-aided retrosynthesis, higher-level retrosynthesis, that focuses on the broader retrosynthetic strategy. We abstract the detailed substructures in the intermediates of a synthetic pathway that do not appear in the target product, allowing our algorithm to emphasize the general, higher-level strategies instead of considering the specifics of chemically-equivalent functional groups. We demonstrate the empirical effectiveness of our approach with higher success rates in single- and multi-step retrosynthetic planning. We further underscore the practical utility of our framework through case studies involving complex drugs and natural products, illustrating how proposed retrosynthetic strategies can be effectively mapped to complete retrosynthetic plans. Ultimately, focusing on higher-level strategies in computer-aided retrosynthesis enhances its effectiveness and enables chemists to leverage their expertise to improve the synthesis design process.

Keywords

Computer-aided synthesis planning
Retrosynthesis
Machine learning

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