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@article{1720496,
author = {Havránková, Eva and PeñaandMéndez, E.M. and Csöllei, Jozef and Havel, Josef},
article_location = {San Diego},
article_number = {February 2021},
doi = {http://dx.doi.org/10.1016/j.bioorg.2020.104565},
keywords = {ANN; Structural descriptors1.3.5-triazinyl sulfonamide derivatives; Carbonic anhydrase},
language = {eng},
issn = {0045-2068},
journal = {Bioorganic Chemistry},
title = {Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors},
url = {https://doi.org/10.1016/j.bioorg.2020.104565},
volume = {107},
year = {2021}
}
TY - JOUR
ID - 1720496
AU - Havránková, Eva - Peña-Méndez, E.M. - Csöllei, Jozef - Havel, Josef
PY - 2021
TI - Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors
JF - Bioorganic Chemistry
VL - 107
IS - February 2021
SP - 1-15
EP - 1-15
PB - Academic Press Inc Elsevier Science
SN - 00452068
KW - ANN
KW - Structural descriptors1.3.5-triazinyl sulfonamide derivatives
KW - Carbonic anhydrase
UR - https://doi.org/10.1016/j.bioorg.2020.104565
L2 - https://doi.org/10.1016/j.bioorg.2020.104565
N2 - 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).
ER -
HAVRÁNKOVÁ, Eva, E.M. PEÑA-MÉNDEZ, Jozef CSÖLLEI and Josef HAVEL. Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors. \textit{Bioorganic Chemistry}. San Diego: Academic Press Inc Elsevier Science, 2021, vol.~107, February 2021, p.~1-15. ISSN~0045-2068. Available from: https://dx.doi.org/10.1016/j.bioorg.2020.104565.
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