Ningthoujam, SS, Nath, R, Kityania, S, Mazumder, PB, Dutta Choudhury, M, Talukdar, AD, Nahar, L and Sarker, SD (2023) R software for QSAR analysis in phytopharmacological studies. Phytochem Anal. ISSN 0958-0344

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	MLR; QSAR; R software; descriptor; feature selection; regression assumption; regression diagnostics; 03 Chemical Sciences; 06 Biological Sciences; 11 Medical and Health Sciences; Analytical Chemistry
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RM Therapeutics. Pharmacology
Divisions:	Pharmacy & Biomolecular Sciences
Publisher:	Wiley
SWORD Depositor:	A Symplectic
Date Deposited:	06 Jul 2023 10:29
Last Modified:	06 Jul 2023 10:30
DOI or ID number:	10.1002/pca.3239
URI:	https://researchonline.ljmu.ac.uk/id/eprint/20227

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