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

Result code in 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>

Result on the web

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DOI - Digital Object Identifier

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Result language

čeština

Original language name

QSAR – MODELOVÁNÍ KVANTITATIVNÍCH VZTAHŮ MEZI STRUKTUROU A AKTIVITOU CHEMICKÝCH LÁTEK

Original language description

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.

Czech name

QSAR – MODELOVÁNÍ KVANTITATIVNÍCH VZTAHŮ MEZI STRUKTUROU A AKTIVITOU CHEMICKÝCH LÁTEK

Czech description

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.

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10401 - Organic chemistry

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2017

Confidentiality

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

Name of the periodical

Chemické listy

ISSN

0009-2770

e-ISSN

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Volume of the periodical

111

Issue of the periodical within the volume

11

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

7

Pages from-to

747-753

UT code for WoS article

000418342800007

EID of the result in the Scopus database

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