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Atom‐to‐atom Mapping : A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/88966

Title: Atom‐to‐atom Mapping : A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies
Authors: Arkadii, Lin Browse this author
Natalia, Dyubankova Browse this author
Timur I., Madzhidov Browse this author
Nugmanov, Ramil I. Browse this author
Verhoeven, Jonas Browse this author
Gimadiev, Timur R. Browse this author
Afonina, Valentina A. Browse this author
Ibragimova, Zarina Browse this author
Rakhimbekova, Assima Browse this author
Sidorov, Pavel Browse this author
Gedich, Andrei Browse this author
Suleymanov, Rail Browse this author
Mukhametgaleev, Ravil Browse this author
Wegner, Joerg Browse this author
Ceulemans, Hugo Browse this author
Varnek, Alexandre Browse this author
Keywords: chemical reactions
data cleaning
atom-to-atom mapping
Issue Date: 2-Nov-2021
Publisher: Wiley-Blackwell
Journal Title: Molecular Informatics
Volume: 41
Issue: 4
Start Page: 2100138
Publisher DOI: 10.1002/minf.202100138
Abstract: In this paper, we compare the most popular Atom-to-Atom Mapping (AAM) tools: ChemAxon,([1]) Indigo,([2]) RDTool,([3]) NameRXN (NextMove),([4]) and RXNMapper([5]) which implement different AAM algorithms. An open-source RDTool program was optimized, and its modified version ("new RDTool") was considered together with several consensus mapping strategies. The Condensed Graph of Reaction approach was used to calculate chemical distances and develop the "AAM fixer" algorithm for an automatized correction of erroneous mapping. The benchmarking calculations were performed on a Golden dataset containing 1851 manually mapped and curated reactions. The best performing RXNMapper program together with the AMM Fixer was applied to map the USPTO database. The Golden dataset, mapped USPTO and optimized RDTool are available in the GitHub repository https://github.com/Laboratoire-de-Chemoinformatique.
Rights: This is the peer reviewed version of the following article: A. Lin, N. Dyubankova, T. I. Madzhidov, R. I. Nugmanov, J. Verhoeven, T. R. Gimadiev, V. A. Afonina, Z. Ibragimova, A. Rakhimbekova, P. Sidorov, A. Gedich, R. Suleymanov, R. Mukhametgaleev, J. Wegner, H. Ceulemans, A. Varnek, Mol. Inf. 2022, 41, 2100138. , which has been published in final form at https://doi.org/10.1002/minf.202100138. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Type: article (author version)
URI: http://hdl.handle.net/2115/88966
Appears in Collections:化学反応創成研究拠点:ICReDD (Institute for Chemical Reaction Design and Discovery : ICReDD) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: Alexandre Varnek

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