Tango*: constrained synthesis planning using chemically informed value functions
Daniel Armstrong, Zlatko Jončev, Jeff Guo, Philippe Schwaller
TL;DR
This paper introduces Tango*, a new method for computer-aided synthesis planning that efficiently finds chemical pathways using specific starting materials.
Contribution
Tango* improves synthesis planning by using a guided search with a computed node cost function, outperforming existing methods.
Findings
Tango* outperforms existing methods in efficiency and solve rate by optimizing a single hyperparameter.
The computed node cost function effectively guides synthesis pathways toward desired starting materials.
Tango* achieves superior performance compared to neural network approaches in constrained synthesis planning.
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. TANGO* enables starting material constrained synthesis planning using computed…
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Taxonomy
TopicsChemistry and Chemical Engineering · Computational Drug Discovery Methods · Process Optimization and Integration