Prediction of HPLC retention factor of potential antituberculotics by QSRR

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11160%2F11%3A10099550" target="_blank" >RIV/00216208:11160/11:10099550 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.tandfonline.com/doi/pdf/10.1080/10826076.2011.545747" target="_blank" >http://www.tandfonline.com/doi/pdf/10.1080/10826076.2011.545747</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/10826076.2011.545747" target="_blank" >10.1080/10826076.2011.545747</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of HPLC retention factor of potential antituberculotics by QSRR

Popis výsledku v původním jazyce

Derivatives of 3-phenyl-2H-1,3-benzoxazine-2,4(3H)-dione (PBOD) have become important mainly as perspective antituberculotic drugs. Quantitative structure-retention relationship (QSRR) is used for predicting the HPLC retention factor of this group of compounds and optimal chromatographic conditions appropriate for this purpose are selected. Among many molecular properties utilizable as the QSRR descriptors, mainly, in silico variables are advantageous as they closely characterize the HPLC retention of the PBOD molecule. Additionally, they are available without a need of the compound synthesis, which is important in the first stage of development of the potential drug. Artificial neural networks (ANN) were successfully used as the basic modeling QSRR tools because their regression outputs allow a direct prediction of retention factors for different combinations of the stationary and mobile phases.

Název v anglickém jazyce

Prediction of HPLC retention factor of potential antituberculotics by QSRR

Popis výsledku anglicky

Derivatives of 3-phenyl-2H-1,3-benzoxazine-2,4(3H)-dione (PBOD) have become important mainly as perspective antituberculotic drugs. Quantitative structure-retention relationship (QSRR) is used for predicting the HPLC retention factor of this group of compounds and optimal chromatographic conditions appropriate for this purpose are selected. Among many molecular properties utilizable as the QSRR descriptors, mainly, in silico variables are advantageous as they closely characterize the HPLC retention of the PBOD molecule. Additionally, they are available without a need of the compound synthesis, which is important in the first stage of development of the potential drug. Artificial neural networks (ANN) were successfully used as the basic modeling QSRR tools because their regression outputs allow a direct prediction of retention factors for different combinations of the stationary and mobile phases.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

FR - Farmakologie a lékárnická chemie

OECD FORD obor

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Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

Journal of Liquid Chromatography and Related Technologies

ISSN

1082-6076

e-ISSN

—

Svazek periodika

34

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

168-181

Kód UT WoS článku

000286895200002

EID výsledku v databázi Scopus

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