R software for QSAR analysis in phytopharmacological studies

0576353 - ÚEB 2024 RIV US eng J - Článek v odborném periodiku
Ningthoujam, S. S. - Nath, R. - Kityania, S. - Mazumder, P. B. - Choudhury, M. D. - Talukdar, A. D. - Nahar, Lutfun - Sarker, S. D.
R software for QSAR analysis in phytopharmacological studies.
Phytochemical Analysis. Roč. 34, č. 7 (2023), s. 709-728. ISSN 0958-0344. E-ISSN 1099-1565
Institucionální podpora: RVO:61389030
Klíčová slova: descriptor * feature selection * mlr * qsar * R software * regression assumption * regression diagnostics
Obor OECD: Biochemical research methods
Impakt faktor: 3.3, rok: 2022
Způsob publikování: Open access
https://doi.org/10.1002/pca.3239

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.
Trvalý link: https://hdl.handle.net/11104/0345922