Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks
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On the basis of the previous models of inductive and steric effects, ‘inductive’ electronegativity and molecular capacitance, a range of new ‘inductive’ QSAR descriptors has been derived. These molecular parameters are easily accessible from electronegativities and covalent radii of the constituent atoms and interatomic distances and can reflect a variety of aspects of intra- and intermolecular interactions. Using 34 ‘inductive’ QSAR descriptors alone we have been able to achieve 93% correct separation of compounds with- and without antibacterial activity (in the set of 657). The elaborated QSAR model based on the Artificial Neural Networks approach has been extensively validated and has confidently assigned antibacterial character to a number of trial antibiotics from the literature.
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Use of molecular descriptor parameters derived from electronegativity and covalent radii of forming atoms and interatomic distances can improve prediction by ANNs.
“The use of molecular descriptor parameters that are derived from electronegativity and covalent radii of forming atoms and interatomic distances can also improve prediction by ANNs [92].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [92]
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- Authors
- Cherkasov, A.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Cherkasov, Artem
- ORCID
- Cherkasov, Artem
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2005-01-31
- Volume
- 6
- Issue
- 1
- Pages
- 63-86
- ISSN
- 1422-0067 (ISSN-L)
- OpenAlex
- W2051160906
- Cited by
- 39
- References
- 39
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2022: 3 · 2021: 3 · 2019: 2 · 2018: 3 · 2016: 3 · 2015: 1 · 2014: 2 · 2013: 1 · 2012: 2
- FWCI
- 2.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Molecular Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
- Keywords
- antibacterial activity, artificial neural networks, inductive effects, electronegativity, covalent radii, interatomic distances, steric effects, molecular descriptors
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