QSAR - Searching for a relationship between a compound’s structure and its biological aktivity. Advances in Chemical Biology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378050%3A_____%2F19%3A00503195" target="_blank" >RIV/68378050:_____/19:00503195 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

QSAR - Searching for a relationship between a compound’s structure and its biological aktivity. Advances in Chemical Biology

Popis výsledku v původním jazyce

Quantitative structure-activity relationship (QSAR) modeling is one of the most popular techniques of virtual screening able to predict the biological activity of small-molecular compounds. Using QSAR classification models, a compound can be labeled as active or inactive on a target, while regression models try to determine its exact activity value. In order to reveal the structure-activity relationships, almost any combination of common machine learning methods (e.g., Support Vector Machines, Random Forest, Neural Networks etc.) with various types of structure descriptors (e.g., physicochemical properties, structural keys, binary fingerprints, etc.) can be utilized. QSAR models are generally fast and are considered as reliable, providing that a correct approach to their validation and an application domain assessment are employed. Nowadays, the techniques of QSAR modeling represent a common part of computational drug design workflows used to detect new biologically active compounds, elucidate their side effects, or assess their potential toxicity risks.

Název v anglickém jazyce

QSAR - Searching for a relationship between a compound’s structure and its biological aktivity. Advances in Chemical Biology

Popis výsledku anglicky

Quantitative structure-activity relationship (QSAR) modeling is one of the most popular techniques of virtual screening able to predict the biological activity of small-molecular compounds. Using QSAR classification models, a compound can be labeled as active or inactive on a target, while regression models try to determine its exact activity value. In order to reveal the structure-activity relationships, almost any combination of common machine learning methods (e.g., Support Vector Machines, Random Forest, Neural Networks etc.) with various types of structure descriptors (e.g., physicochemical properties, structural keys, binary fingerprints, etc.) can be utilized. QSAR models are generally fast and are considered as reliable, providing that a correct approach to their validation and an application domain assessment are employed. Nowadays, the techniques of QSAR modeling represent a common part of computational drug design workflows used to detect new biologically active compounds, elucidate their side effects, or assess their potential toxicity risks.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LO1220" target="_blank" >LO1220: CZ-OPENSCREEN: Národní infrastruktura chemické biologie</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Advances in Chemical Biology

ISBN

978-80-88011-03-3

Počet stran výsledku

9

Strana od-do

187-196

Počet stran knihy

210

Název nakladatele

OPTIO CZ

Místo vydání

Praha

Kód UT WoS kapitoly

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