QSAR Applied to 4-Chloro-3-formylcoumarin Derivatives Targeting Human Thymidine Phosphorylase

Scior, Thomas; Garcia-Hernandez, Juan Carlos; Abdallah, Hassan H.; Alexander, Christian

doi:10.1016/j.ccmp.2022.100031

Cited by 1 publication

(2 citation statements)

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“…57 The potential of 4-chloro-3-formylcoumarin derivatives targeting human thymidine phosphorylase was analyzed by R-based QSAR. 58 Some more QSAR-based studies with phytochemicals are listed in Table 3.…”

Section: Example Of Qsar With Specific Phytochemicalsmentioning

confidence: 99%

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R software for QSAR analysis in phytopharmacological studies

Ningthoujam¹,

Nath

Kityania

et al. 2023

Phytochemical Analysis

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IntroductionIn 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.ObjectiveThe 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.ResultsThe 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.ConclusionQSAR 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.

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Section: Example Of Qsar With Specific Phytochemicalsmentioning

confidence: 99%

“…The potential of 4‐chloro‐3‐formylcoumarin derivatives targeting human thymidine phosphorylase was analyzed by R‐based QSAR 58 …”

Section: Example Of Qsar With Specific Phytochemicalsmentioning

confidence: 99%