Expert System for Predicting Reaction Conditions: The Michael Reaction Case
Abstract
A generic chemical transformation may often be achieved under various synthetic conditions. However, for any specific reagents, only one or a few among the reported synthetic protocols may be successful. For example, Michael β-addition reactions may proceed under different choices of solvent (e.g., hydrophobic, aprotic polar, protic) and catalyst (e.g., Brønsted acid, Lewis acid, Lewis base, etc.). Chemoinformatics methods could be efficiently used to establish a relationship between the reagent structures and the required reaction conditions, which would allow synthetic chemists to waste less time and resources in trying out various protocols in search for the appropriate one. In order to address this problem, a number of 2-classes classification models have been built on a set of 198 Michael reactions retrieved from literature. Trained models discriminate between processes that are compatible and respectively processes not feasible under a specific reaction condition option (feasible or not with a Lewis acid catalyst, feasible or not in hydrophobic solvent, etc.). Eight distinct models were built to decide the compatibility of a Michael addition process with each considered reaction condition option, while a ninth model was aimed to predict whether the assumed Michael addition is feasible at all. Different machine-learning methods (Support Vector Machine, Naive Bayes, and Random Forest) in combination with different types of descriptors (ISIDA fragments issued from Condensed Graphs of Reactions, MOLMAP, Electronic Effect Descriptors, and Chemistry Development Kit computed descriptors) have been used. Models have good predictive performance in 3-fold cross-validation done three times: balanced accuracy varies from 0.7 to 1. Developed models are available for the users at http://infochim.u-strasbg.fr/webserv/VSEngine.html . Eventually, these were challenged to predict feasibility conditions for ∼50 novel Michael reactions from the eNovalys database (originally from patent literature).
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Marcou et al. used CGR-derived fragment descriptors to train SVM, RF, and Naive Bayes classification models to predict optimal solvents and catalysts for the Michael reaction.
“For example, Marcou et al.162 used CGR-derived fragment descriptors to train SVM, RF, and Naive Bayes classification models to predict optimal solvents and catalysts for the Michael reaction.”
From QSAR without borders (2020) · cites this paper as [162]
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- Authors
- Marcou, G., Aires de Sousa, J., Latino, D. A. R. S., de Luca, A., Horvath, D., Rietsch, V., & Varnek, A.
- Affiliations
- Université de Strasbourg (FR)
- Departamento de Quı́mica and REQUIMTE, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa 2829-516 Caparica, Portugal
- Universidade Nova de Lisboa (PT)
- Laboratory of Chemoinformatics, University of Strasbourg, 1 rue B. Pascal, 67000 Strasbourg, France
- eNovalys, 1 Rue Jean Sapidus, Batiment Pythagore, 67400 Illkirch, France
- Kazan Federal University (RU)
- Corresponding author
- Marcou, G.
- Varnek, A.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-02-03
- Volume
- 55
- Issue
- 2
- Pages
- 239-250
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2052882499
- Cited by
- 86
- References
- 48
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 10 · 2023: 12 · 2022: 7 · 2021: 15 · 2020: 10 · 2019: 6 · 2018: 8 · 2017: 4 · 2016: 4
- FWCI
- 5.50
- 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
- Fundação para a Ciência e a Tecnologia (28728QF, PEst-C/EQB/LA0006/2013, SFRH/BPD/63192/2009)
- Ministère des Affaires étrangères (28728QF)
- Région Alsace
- Russian Scientific Foundation (14-43-00024)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › History and advancements in chemistry
- Keywords
- Michael addition, reaction condition prediction, cheminformatics, expert systems, Lewis acid catalysis, hydrophobic solvents, reaction feasibility, machine learning
- MeSH terms
- Machine Learning, Algorithms, Bayes Theorem, Catalysis, Chemistry, Organic, Expert Systems, Indicators and Reagents, Models, Chemical, Predictive Value of Tests, Reproducibility of Results, Databases, Factual, Quantitative Structure-Activity Relationship, Informatics, Support Vector Machine
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