QSAR based on hybrid optimal descriptors as a tool to predict antibacterial activity against Staphylococcus aureus

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F22%3A10445176" target="_blank" >RIV/00216208:11310/22:10445176 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=VipYfy5clD" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=VipYfy5clD</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.31083/j.fbl2704112" target="_blank" >10.31083/j.fbl2704112</a>

Jazyk výsledku

angličtina

Název v původním jazyce

QSAR based on hybrid optimal descriptors as a tool to predict antibacterial activity against Staphylococcus aureus

Popis výsledku v původním jazyce

Background: Staphylococcus aureus bacterial infections are still a serious health care problem. Therefore, the development of new drugs for these infections is a constant requirement. Quantitative structure-activity relationship (QSAR) methods can assist this development. Methods: The study included 151 structurally diverse compounds with antibacterial activity against S. aureus ATCC 25923 (Endpoint 1) or the drug-resistant clinical isolate of S. aureus (Endpoint 2). QSARs based on hybrid optimal descriptors were used. Results: The predictive potential of developed models has been checked with three random splits into training, passive training, calibration, and validation sets. The proposed models give satisfactory predictive models for both endpoints examined. Conclusions: The results of the study show the possibility of SMILES-based QSAR in the evaluation of the antibacterial activity of structurally diverse compounds for both endpoints. Although the developed models give satisfactory predictive models for both endpoints examined, splitting has an apparent influence on the statistical quality of the models.

Název v anglickém jazyce

QSAR based on hybrid optimal descriptors as a tool to predict antibacterial activity against Staphylococcus aureus

Popis výsledku anglicky

Background: Staphylococcus aureus bacterial infections are still a serious health care problem. Therefore, the development of new drugs for these infections is a constant requirement. Quantitative structure-activity relationship (QSAR) methods can assist this development. Methods: The study included 151 structurally diverse compounds with antibacterial activity against S. aureus ATCC 25923 (Endpoint 1) or the drug-resistant clinical isolate of S. aureus (Endpoint 2). QSARs based on hybrid optimal descriptors were used. Results: The predictive potential of developed models has been checked with three random splits into training, passive training, calibration, and validation sets. The proposed models give satisfactory predictive models for both endpoints examined. Conclusions: The results of the study show the possibility of SMILES-based QSAR in the evaluation of the antibacterial activity of structurally diverse compounds for both endpoints. Although the developed models give satisfactory predictive models for both endpoints examined, splitting has an apparent influence on the statistical quality of the models.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10406 - Analytical chemistry

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Frontiers in Bioscience - Landmark

ISSN

2768-6701

e-ISSN

2768-6698

Svazek periodika

27

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

13

Strana od-do

112

Kód UT WoS článku

000797787300008

EID výsledku v databázi Scopus

2-s2.0-85128844118