Tango*: constrained synthesis planning using chemically informed value functions

Computer-aided synthesis planning (CASP) has made significant strides in generating retrosynthetic pathways for simple molecules in a non-constrained fashion. Recent work has introduced specialized bidirectional search algorithms to find synthesis pathways that incorporate pre-selected starting materials, tackling a specific formulation of the starting material-constrained problem. In this work, we introduce a simple guided search-Tango*-which allows solving the starting material-constrained synthesis planning problem using an existing unidirectional search algorithm, Retro*. We show that by optimising a single hyperparameter, Tango* outperforms existing methods in terms of efficiency and solve rate. We also highlight the effectiveness of our computed node cost function in steering synthesis pathways.

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Publication Details

Journal
Digital Discovery
Published
2025-01-01
DOI
https://doi.org/10.1039/d5dd00130g
Primary Topic
Chemistry and Chemical Engineering
Type
article
Field-Weighted Citation Impact
0.00

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article

Tango*: constrained synthesis planning using chemically informed value functions

Jeff Guo, Philippe Schwaller, D. M. Armstrong, Zlatko Jončev
2 links
Digital Discovery
Chemistry and Chemical Engineering
article

Tango*: constrained synthesis planning using chemically informed value functions

Jeff Guo, Philippe Schwaller, D. M. Armstrong, Zlatko Jončev
article en

Abstract

Computer-aided synthesis planning (CASP) has made significant strides in generating retrosynthetic pathways for simple molecules in a non-constrained fashion. Recent work has introduced specialized bidirectional search algorithms to find synthesis pathways that incorporate pre-selected starting materials, tackling a specific formulation of the starting material-constrained problem. In this work, we introduce a simple guided search-Tango*-which allows solving the starting material-constrained synthesis planning problem using an existing unidirectional search algorithm, Retro*. We show that by optimising a single hyperparameter, Tango* outperforms existing methods in terms of efficiency and solve rate. We also highlight the effectiveness of our computed node cost function in steering synthesis pathways.

Digital DiscoveryVol. 4(9)
NCCR Chemical Biology - Visualisation and Control of Biological Processes Using Chemistry (CH), École Polytechnique Fédérale de Lausanne (CH)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Sustainable cities and communities
Openalex Percentile: Top 82%
Chemistry and Chemical Engineering
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