Leveraging the Condensed Graph of Reaction for Clustering Retrosynthetic Pathways.

Authors

Almaz Gilmullin, Tagir Akhmetshin, Dmitry Zankov, Olga Klimchuk, Dragos Horvath, Timur Madzhidov, Alexandre Varnek

Published in

Journal of chemical information and modeling. Volume 66. Issue 17. Pages 10919-10934. Sep 14, 2026.

Abstract

Modern retrosynthetic tools can propose hundreds of alternative pathways for a single target, making it challenging to effectively explore and navigate the resulting route space. We present a CGR-based framework for the analysis and clustering of synthetic routes that integrates both target-centered and all-species-centered perspectives. Entire reaction pathways are encoded as single-molecule graphs (RouteCGR) or reduced representations retaining only target atoms (SB-CGR), enabling automatic identification of strategic bond patterns (SBPs). These representations can be transformed into Morgan fingerprints for a quantitative route similarity assessment. We propose a two-level clustering strategy in which routes are first grouped by shared SBPs, ensuring high interpretability based on key retrosynthetic disconnections, and then further differentiated using RouteCGR similarity to capture variations in starting materials and auxiliary transformations. The method demonstrates near-linear scalability and computational efficiency for large data sets. Application to synthetic routes for apatinib generated by multiple planning tools reveals tool-dependent diversity in strategic disconnections and highlights the benefit of combining tools to expand route space. The framework also supports cross-target analysis, enabling the identification of reusable route families that share common strategic disconnections and building blocks across related molecules. Overall, the SBP-based approach provides an interpretable and scalable solution for automated synthesis route analysis and informed decision-making. The proposed approach is implemented in SynPlanner retrosynthesis planning software and is available as a standalone module at https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner.

PMID:
42734524
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.

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