• SynKit: A Graph-Based Python F...

Treffer: SynKit: A Graph-Based Python Framework for Rule-Based Reaction Modeling and Analysis.

Title:
SynKit: A Graph-Based Python Framework for Rule-Based Reaction Modeling and Analysis.
Authors:
Phan TL; Bioinformatics Group, Department of Computer Science & Interdisciplinary Center for Bioinformatics & School for Embedded and Composite Artificial Intelligence (SECAI), Leipzig University, Härtelstraße 16-18, D-04107 Leipzig, Germany.; Department of Mathematics and Computer Science, University of Southern Denmark, DK-5230 Odense M, Denmark., González Laffitte ME; Bioinformatics Group, Department of Computer Science & Interdisciplinary Center for Bioinformatics & School for Embedded and Composite Artificial Intelligence (SECAI), Leipzig University, Härtelstraße 16-18, D-04107 Leipzig, Germany.; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig University, D-04103 Leipzig, Germany., Weinbauer K; Bioinformatics Group, Department of Computer Science & Interdisciplinary Center for Bioinformatics & School for Embedded and Composite Artificial Intelligence (SECAI), Leipzig University, Härtelstraße 16-18, D-04107 Leipzig, Germany.; Machine Learning Research Unit, TU Wien Informatics, A-1040 Wien, Austria., Merkle D; Department of Mathematics and Computer Science, University of Southern Denmark, DK-5230 Odense M, Denmark.; Algorithmic Cheminformatics Group, Faculty of Technology & Center for Biotechnology (CeBiTec), Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany., Andersen JL; Department of Mathematics and Computer Science, University of Southern Denmark, DK-5230 Odense M, Denmark., Fagerberg R; Department of Mathematics and Computer Science, University of Southern Denmark, DK-5230 Odense M, Denmark., Gatter T; Bioinformatics Group, Department of Computer Science & Interdisciplinary Center for Bioinformatics & School for Embedded and Composite Artificial Intelligence (SECAI), Leipzig University, Härtelstraße 16-18, D-04107 Leipzig, Germany., Stadler PF; Bioinformatics Group, Department of Computer Science & Interdisciplinary Center for Bioinformatics & School for Embedded and Composite Artificial Intelligence (SECAI), Leipzig University, Härtelstraße 16-18, D-04107 Leipzig, Germany.; Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany.; Department of Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Vienna, Austria.; Facultad de Ciencias, Universidad National de Colombia, Bogotá CO-111321, Colombia.; Center for non-coding RNA in Technology and Health, University of Copenhagen, Ridebanevej 9, DK-1870 Frederiksberg, Denmark.; Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, New Mexico 87501, United States.
Source:
Journal of chemical information and modeling [J Chem Inf Model] 2025 Dec 22; Vol. 65 (24), pp. 13012-13019. Date of Electronic Publication: 2025 Dec 03.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE
Imprint Name(s):
Original Publication: Washington, D.C. : American Chemical Society, c2005-
MeSH Terms:
Software* , Models, Chemical* , Programming Languages*
References:
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J Chem Inf Model. 2022 Nov 28;62(22):5513-5524. (PMID: 36326605)
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J Chem Inf Model. 2025 Jan 13;65(1):15-21. (PMID: 39739735)
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Entry Date(s):
Date Created: 20251203 Date Completed: 20251222 Latest Revision: 20251226
Update Code:
20251226
PubMed Central ID:
PMC12728950
DOI:
10.1021/acs.jcim.5c02123
PMID:
41337644
Database:
MEDLINE

Weitere Informationen

Computational modeling of chemical reactions is fundamental to modern synthetic chemistry but is often hindered by a fragmented software ecosystem and the complexity of accurately representing the reaction mechanisms. To address this, we introduce SynKit, an open-source Python library that provides a unified, chemically intuitive framework for reaction informatics. SynKit performs core tasks such as reaction canonicalization and transformation classification, while other functionalities─such as synthetic route construction through rule composition─are supported through integration with external libraries. The newly introduced Mechanistic Transition Graph extends the traditional net-change representation of the Imaginary Transition State by explicitly modeling the sequence of bond-forming and bond-breaking events, capturing transient intermediates, and providing deeper mechanistic insight. Designed for easy installation and broad compatibility, SynKit integrates smoothly into existing computational workflows for exploring complex Chemical Reaction Networks . For more advanced network analyses, it interfaces with specialized tools (e.g., MØD) to support exhaustive mechanism enumeration and kinetics-aware studies. By combining advanced mechanistic modeling with an accessible, modular design, SynKit supports more reproducible and rigorous research in automated synthesis planning.