QSAR – MODELOVÁNÍ KVANTITATIVNÍCH VZTAHŮ MEZI STRUKTUROU A AKTIVITOU CHEMICKÝCH LÁTEK

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F17%3A43914572" target="_blank" >RIV/60461373:22310/17:43914572 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

čeština

Název v původním jazyce

QSAR – MODELOVÁNÍ KVANTITATIVNÍCH VZTAHŮ MEZI STRUKTUROU A AKTIVITOU CHEMICKÝCH LÁTEK

Popis výsledku v původním jazyce

Quantitative structure-activity relationship (QSAR) modelling is one of the most popular techniques of virtual screening used to predict the activity of a compound toward a biological target. While QSAR classification models are able to predict whether a compound is active or inactive (class) toward a target, regression models try to predict its exact activity value. To find the relationship between the structure and activity of a compound, common machine learning methods are employed (e.g., Support Vector Machines, Random Forest, Neural Networks etc.) together with diverse types of compound descriptors (e.g., physico-chemical properties, structural keys, binary fingerprints etc.). QSAR models are generally very fast and, when a correct approach to their validation and applicability domain setting is used, also reliable. They became a common part of computational drug design work-flows employed to detect new drug candidates, elucidate their side/adverse effects or assess their potential toxicity risks.

Název v anglickém jazyce

QSAR - Modelling of Quantitative Relations between Structure and Activity of Chemical Compounds

Popis výsledku anglicky

Quantitative structure-activity relationship (QSAR) modelling is one of the most popular techniques of virtual screening used to predict the activity of a compound toward a biological target. While QSAR classification models are able to predict whether a compound is active or inactive (class) toward a target, regression models try to predict its exact activity value. To find the relationship between the structure and activity of a compound, common machine learning methods are employed (e.g., Support Vector Machines, Random Forest, Neural Networks etc.) together with diverse types of compound descriptors (e.g., physico-chemical properties, structural keys, binary fingerprints etc.). QSAR models are generally very fast and, when a correct approach to their validation and applicability domain setting is used, also reliable. They became a common part of computational drug design work-flows employed to detect new drug candidates, elucidate their side/adverse effects or assess their potential toxicity risks.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10401 - Organic chemistry

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Chemické listy

ISSN

0009-2770

e-ISSN

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Svazek periodika

111

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

7

Strana od-do

747-753

Kód UT WoS článku

000418342800007

EID výsledku v databázi Scopus

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