R software for QSAR analysis in phytopharmacological studies

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389030%3A_____%2F23%3A00576353" target="_blank" >RIV/61389030:_____/23:00576353 - isvavai.cz</a>

Alternative codes found

RIV/61989592:15310/23:73622886

Result on the web

<a href="https://doi.org/10.1002/pca.3239" target="_blank" >https://doi.org/10.1002/pca.3239</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/pca.3239" target="_blank" >10.1002/pca.3239</a>

Result language

angličtina

Original language name

R software for QSAR analysis in phytopharmacological studies

Original language description

Introduction: In recent decades, quantitative structure–activity relationship (QSAR) analysis has become an important method for drug design and natural product research. With the availability of bioinformatic and cheminformatic tools, a vast number of descriptors have been generated, making it challenging to select potential independent variables that are accurately related to the dependent response variable. Objective: The objective of this study is to demonstrate various descriptor selection procedures, such as the Boruta approach, all subsets regression, the ANOVA approach, the AIC method, stepwise regression, and genetic algorithm, that can be used in QSAR studies. Additionally, we performed regression diagnostics using R software to test parameters such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity. Results: The workflow designed in this study highlights the different descriptor selection procedures and regression diagnostics that can be used in QSAR studies. The results showed that the Boruta approach and genetic algorithm performed better than other methods in selecting potential independent variables. The regression diagnostics parameters tested using R software, such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity, helped in identifying and diagnosing model errors, ensuring the reliability of the QSAR model. Conclusion: QSAR analysis is vital in drug design and natural product research. To develop a reliable QSAR model, it is essential to choose suitable descriptors and perform regression diagnostics. This study offers an accessible, customizable approach for researchers to select appropriate descriptors and diagnose errors in QSAR studies.

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

—

OECD FORD branch

10609 - Biochemical research methods

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Phytochemical Analysis

ISSN

0958-0344

e-ISSN

1099-1565

Volume of the periodical

34

Issue of the periodical within the volume

7

Country of publishing house

US - UNITED STATES

Number of pages

20

Pages from-to

709-728

UT code for WoS article

001020077900001

EID of the result in the Scopus database

2-s2.0-85164190073