Variable Elimination Approaches for Data-Noise Reduction in 3D QSAR Calculations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F15%3A50003638" target="_blank" >RIV/62690094:18450/15:50003638 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00179906:_____/15:10319397

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-23485-4_33" target="_blank" >http://dx.doi.org/10.1007/978-3-319-23485-4_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-23485-4_33" target="_blank" >10.1007/978-3-319-23485-4_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Variable Elimination Approaches for Data-Noise Reduction in 3D QSAR Calculations

Popis výsledku v původním jazyce

In the last several decades, the drug research has moved to involve various IT technologies in order to rationalize the design of novel bioactive chemical compounds. An important role among these computer-aided drug design (CADD) methods is played by a technique known as quantitative structureactivity relationship (QSAR). The approach is utilized to find a statistically significant model correlating the biological activity with more or less extent data derived from the chemical structures. The present article deals with approaches for discriminating unimportant information in the data input within the three dimensional variant of QSAR - 3D QSAR. Special attention is turned to uninformative and iterative variable elimination (UVE/IVE) methods applicablein connection with partial least square regression (PLS). Herein, we briefly introduce 3D QSAR approach by analyzing 30 antituberculotics. The analysis is examined by four UVE/IVE-PLS based data-noise reduction methods.

Název v anglickém jazyce

Variable Elimination Approaches for Data-Noise Reduction in 3D QSAR Calculations

Popis výsledku anglicky

In the last several decades, the drug research has moved to involve various IT technologies in order to rationalize the design of novel bioactive chemical compounds. An important role among these computer-aided drug design (CADD) methods is played by a technique known as quantitative structureactivity relationship (QSAR). The approach is utilized to find a statistically significant model correlating the biological activity with more or less extent data derived from the chemical structures. The present article deals with approaches for discriminating unimportant information in the data input within the three dimensional variant of QSAR - 3D QSAR. Special attention is turned to uninformative and iterative variable elimination (UVE/IVE) methods applicablein connection with partial least square regression (PLS). Herein, we briefly introduce 3D QSAR approach by analyzing 30 antituberculotics. The analysis is examined by four UVE/IVE-PLS based data-noise reduction methods.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Progress in artificial intelligence (EPIA 2015)

ISBN

978-3-319-23484-7

ISSN

—

e-ISSN

—

Počet stran výsledku

13

Strana od-do

313-325

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Portugal, Coimbra

Datum konání akce

8. 9. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000363570000033