Automatized Assessment of Protective Group Reactivity: A Step Toward Big Reaction Data Analysis
Abstract
We report a new method to assess protective groups (PGs) reactivity as a function of reaction conditions (catalyst, solvent) using raw reaction data. It is based on an intuitive similarity principle for chemical reactions: similar reactions proceed under similar conditions. Technically, reaction similarity can be assessed using the Condensed Graph of Reaction (CGR) approach representing an ensemble of reactants and products as a single molecular graph, i.e., as a pseudomolecule for which molecular descriptors or fingerprints can be calculated. CGR-based in-house tools were used to process data for 142,111 catalytic hydrogenation reactions extracted from the Reaxys database. Our results reveal some contradictions with famous Greene's Reactivity Charts based on manual expert analysis. Models developed in this study show high accuracy (ca. 90%) for predicting optimal experimental conditions of protective group deprotection.
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Reaction data from public databases is of mixed quality.
“Generally, reaction data from public databases is of mixed quality.”
From QSAR without borders (2020) · cites this paper as [139]
Many reactions are stoichiometrically unbalanced, some important data on reaction conditions are missing, and different names are used for the same catalysts or solvents.
“Many of the reactions are stoichiometrically unbalanced, some important data on reaction conditions are missing, and different names are used for the same catalysts or solvents.139”
From QSAR without borders (2020) · cites this paper as [139]
Lin et al. used the heuristic that similar reactions proceed under similar conditions to predict optimal reaction conditions.
“Lin et al.139 used the heuristic that similar reactions proceed under similar conditions to predict optimal reaction conditions.”
From QSAR without borders (2020) · cites this paper as [139]
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- Authors
- Lin, A. I., Madzhidov, T. I., Klimchuk, O., Nugmanov, R. I., Antipin, I. S., & Varnek, A.
- Affiliations
- Kazan Federal University (RU)
- Université de Strasbourg (FR)
- Laboratory of Chemoinformatics and Molecular Modeling, Department of Organic Chemistry, A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kremlyovskaya Str. 18, Kazan, Russia, 420008
- Corresponding author
- Varnek, Alexandre
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-11-08
- Volume
- 56
- Issue
- 11
- Pages
- 2140-2148
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2540880742
- Cited by
- 51
- References
- 26
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 5 · 2023: 3 · 2022: 2 · 2021: 13 · 2020: 5 · 2019: 8 · 2018: 8
- FWCI
- 4.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- zvestiya of the National Academy of Sciences of Belarus (National Academy of Sciences of Belarus) (submittedVersion)
- Funders
- Russian Science Foundation (14-43-00024)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- molecular graphs, catalytic hydrogenation, reaction condition prediction
- MeSH terms
- Automation, Catalysis, Hydroxides, Models, Chemical, Phenols, Solvents, Databases, Factual, Informatics
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