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

作者：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 · 发表于：Molecular Informatics · 年份：2021 · DOI：10.1002/minf.202100138 · 被引用次数：74 · 研究领域：Machine Learning in Materials Science、Computational Drug Discovery Methods、Protein Structure and Dynamics

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.