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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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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

10406 - Analytical chemistry

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

Frontiers in Bioscience - Landmark

ISSN

2768-6701

e-ISSN

2768-6698

Volume of the periodical

27

Issue of the periodical within the volume

4

Country of publishing house

SG - SINGAPORE

Number of pages

13

Pages from-to

112

UT code for WoS article

000797787300008

EID of the result in the Scopus database

2-s2.0-85128844118