Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F21%3A00075151" target="_blank" >RIV/00159816:_____/21:00075151 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14160/21:00120916

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0045206820318630?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0045206820318630?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bioorg.2020.104565" target="_blank" >10.1016/j.bioorg.2020.104565</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors

Popis výsledku anglicky

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).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10608 - Biochemistry and molecular biology

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

BIOORGANIC CHEMISTRY

ISSN

0045-2068

e-ISSN

—

Svazek periodika

107

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

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Kód UT WoS článku

000618103400003

EID výsledku v databázi Scopus

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