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Atom‐to‐atom Mapping : A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies
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)
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Submitter: Alexandre Varnek
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