R software for QSAR analysis in phytopharmacological studies.

Autor: Ningthoujam SS; Government Hindi Teachers' Training College, Imphal, Manipur, India., Nath R; Department of Life Science and Bioinformatics, Assam University, Silchar, Assam, India., Kityania S; Department of Life Science and Bioinformatics, Assam University, Silchar, Assam, India., Mazumder PB; Department of Biotechnology, Assam University, Silchar, Assam, India., Dutta Choudhury M; Department of Life Science and Bioinformatics, Assam University, Silchar, Assam, India., Talukdar AD; Department of Life Science and Bioinformatics, Assam University, Silchar, Assam, India., Nahar L; Laboratory of Growth Regulators, Institute of Experimental Botany, The Czech Academy of Sciences and Palacký University, Olomouc, Czech Republic., Sarker SD; Centre for Natural Products Discovery (CNPD), School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK.
Jazyk: angličtina
Zdroj: Phytochemical analysis : PCA [Phytochem Anal] 2023 Oct; Vol. 34 (7), pp. 709-728. Date of Electronic Publication: 2023 Jul 01.
DOI: 10.1002/pca.3239
Abstrakt: 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.
(© 2023 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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