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

Arkadii Lin, Natalia Dyubankova, Timur Madzhidov, Ramil Nugmanov, Jonas Verhoeven, Timur Gimadiev, Valentina A. Afonina, Zarina Ibragimova, Assima Rakhimbekova, Pavel Sidorov, Andrei Gedich, Rail Suleymanov, Ravil N. Mukhametgaleev, Jörg K. Wegner, Hugo Ceulemans, Alexandre Varnek

2021Molecular Informatics74 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

BenchmarkingComputer scienceAtom (system on chip)AlgorithmGraphData miningArtificial intelligenceTheoretical computer scienceParallel computingMarketingBusinessMachine Learning in Materials ScienceComputational Drug Discovery MethodsProtein Structure and Dynamics