Structure–reactivity modeling using mixture-based representation of chemical reactions
Abstract
We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn't need an explicit labeling of a reaction center. The rigorous "product-out" cross-validation (CV) strategy has been suggested. Unlike the naïve "reaction-out" CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new "mixture" approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.
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- Authors
- Polishchuk, P., Madzhidov, T., Gimadiev, T., Bodrov, A., Nugmanov, R., & Varnek, A.
- Affiliations
- Kazan Federal University (RU)
- Corresponding author
- Madzhidov, Timur
- Varnek, Alexandre
- Venue
- Journal of Computer-Aided Molecular Design
- Published
- 2017-07-27
- Volume
- 31
- Issue
- 9
- Pages
- 829-839
- ISSN
- 0920-654X (ISSN-L)
- 1573-4951
- OpenAlex
- W2739908447
- Cited by
- 27
- References
- 34
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 5 · 2020: 4 · 2019: 6 · 2018: 5
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computer-Aided Molecular Design (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Russian Science 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 › Organic Chemistry › Chemical Reaction Mechanisms
- Keywords
- chemical reaction representation, SIRM, rate constants, E2 reaction
- MeSH terms
- Kinetics, Models, Molecular, Organic Chemicals, Molecular Structure, Quantitative Structure-Activity Relationship
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