Cited by 36Computational Drug Discovery MethodsMachine Learning in Materials ScienceOrganic Chemistry Cycloaddition Reactions

Predictive Models for Kinetic Parameters of Cycloaddition Reactions

Open access
Glavatskikh, Marta, Madzhidov, Timur, Horvath, Dragos, Nugmanov, Ramil, Gimadiev, Timur, Malakhova, Daria, Marcou, Gilles, Varnek, Alexandre·2019·Molecular Informatics

Abstract

This paper reports SVR (Support Vector Regression) and GTM (Generative Topographic Mapping) modeling of three kinetic properties of cycloaddition reactions: rate constant (log k), activation energy (Ea) and pre‐exponential factor (logA). A data set of 1849 reactions, comprising (4+2), (3+2) and (2+2) cycloadditions (CA) were studied in different solvents and at different temperatures. The reactions were encoded by the ISIDA fragment descriptors generated for Condensed Graph of Reaction (CGR). For a given reaction, a CGR condenses structures of all the reactants and products into one single molecular graph, described both by conventional chemical bonds and “dynamical” bonds characterizing chemical transformations. Different scenarios of log k assessment were exploited: direct modeling, application of the Arrhenius equation and temperature‐scaled GTM landscapes. The log k models with optimal cross‐validated statistics (Q 2=0.78–0.94 RMSE=0.45–0.86) have been challenged to predict rates for the external test set of 200 reactions, comprising both reactions that were not present in the training set, and training set transformations performed under different reaction conditions. The models are freely available on our web‐server: http://cimm.kpfu.ru/models.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/minf.201800077

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Fragment descriptors generated for CGRs were concatenated with solvent and temperature descriptors and used to train logk models for bimolecular nucleophilic substitution, bimolecular elimination, and different types of cycloaddition.

    “Fragment descriptors generated for CGRs were concatenated with solvent and temperature descriptors and used to train logk models for bimolecular nucleophilic substitution,159 bimolecular elimination, and different types of cycloaddition.160”

    From QSAR without borders (2020) · cites this paper as [160]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Glavatskikh, M., Madzhidov, T., Horvath, D., Nugmanov, R., Gimadiev, T., Malakhova, D., Marcou, G., & Varnek, A.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • Kazan Federal University (RU)
  • Laboratory of Chemoinformatics and Molecular Modeling Butlerov Institute of Chemistry Kazan Federal University Kremlyovskaya str. 18 Kazan Russia
Corresponding author
Varnek, Alexandre
Venue
Molecular Informatics
Publisher
Wiley
Published
2018-08-22
Volume
38
Issue
1-2
Pages
e1800077-e1800077
ISSN
  • 1868-1743 (ISSN-L)
  • 1868-1751
OpenAlex
W2888302950
Cited by
36
References
1
Citations by year
2026: 1 · 2025: 4 · 2023: 7 · 2022: 3 · 2021: 7 · 2020: 7 · 2019: 6 · 2018: 1
FWCI
3.93
Type
journal article
Language
en
Open access
Locations
  • Molecular Informatics (publishedVersion)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, other-oa)
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 › Organic Chemistry Cycloaddition Reactions
Keywords
cycloaddition, reaction kinetics, rate constant prediction, activation energy, pre-exponential factor, support vector regression, generative topographic mapping, Diels-Alder reaction, [3+2] cycloaddition, [2+2] cycloaddition, solvent effects, temperature effects, reaction rate prediction
MeSH terms
Kinetics, Models, Chemical, Cycloaddition Reaction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review