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Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors
E. Havránková, EM. Peña-Méndez, J. Csöllei, J. Havel
Bioorganic chemistry.
2021 ;
107 (-) :
104565.
[pub] 20201219
Language English Country United States
Document type Journal Article
Persistent link
https://www.medvik.cz/link/bmc21026099
- MeSH
- Antigens, Neoplasm metabolism MeSH
- Carbonic Anhydrase Inhibitors chemistry metabolism MeSH
- Carbonic Anhydrase II antagonists & inhibitors metabolism MeSH
- Carbonic Anhydrase IX antagonists & inhibitors metabolism MeSH
- Humans MeSH
- Neural Networks, Computer * MeSH
- Drug Design MeSH
- Sulfonamides chemistry metabolism MeSH
- Triazines chemistry MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Simple molecular descriptors of extensive series of 1,3,5-triazinyl sulfonamide derivatives, based on the structure of sulfonamides and their physicochemical properties, were designed and calculated. These descriptors were successfully applied as inputs for artificial neural network (ANN) modelling of the relationship between the structure and biological activity. The optimized ANN architecture was applied to the prediction of the inhibition activity of 1,3,5-triazinyl sulfonamides against human carbonic anhydrase (hCA) II, tumour-associated hCA IX, and their selectivity (hCA II/hCA IX).
References provided by Crossref.org
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