Tango*: Constrained synthesis planning using chemically informed value functions

TL;DR

This paper introduces Tango*, a chemically informed, hyperparameter-optimized cost function that enhances the efficiency and success rate of constrained synthesis planning using Retro*, outperforming existing methods.

Contribution

It presents a simple guided search approach with a hyperparameter-tuned cost function, improving constrained synthesis planning without needing a new search algorithm.

Findings

Tango* outperforms existing methods in efficiency and success rate.

Tango* achieves lower computation times with similar route lengths.

The cost function enhances bidirectional DESP methods and surpasses neural guided search methods.

Abstract

Computer-aided synthesis planning (CASP) has made significant strides in generating retrosynthetic pathways for simple molecules in a non-constrained fashion. Recent work introduces a specialised bidirectional search algorithm with forward and retro expansion to address the starting material-constrained synthesis problem, allowing CASP systems to provide synthesis pathways from specified starting materials, such as waste products or renewable feed-stocks. In this work, we introduce a simple guided search which allows solving the starting material-constrained synthesis planning problem using an existing, uni-directional search algorithm, Retro*. We show that by optimising a single hyperparameter, Tango* outperforms existing methods in terms of efficiency and solve rate. We find the Tango* cost function catalyses strong improvements for the bidirectional DESP methods. Our method also achieves lower wall clock times while proposing synthetic routes of similar length, a common metric for route quality. Finally, we highlight potential reasons for the strong performance of Tango over neural guided search methods