Journal of Drug Metabolism & Toxicology
Open Access

Departamento de Investigación y Desarrollo, Los Alamos National Laboratory, Valdivia, Chile

Publications

• Research Article   
  Accurate, fast and easy-to-understand Density Functional Tight Binding and Machine Learning QSAR for the DPPH and ABTS antioxidant activity of phenolic compounds based on No-Code Freeware
  Author(s): Andrés Halabi*, Millaray Hernández and Hans Lenes
  
  In this study, we developed various Quantitative Structure-Activity Relationship (QSAR) models for the 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid (ABTS) experimental values of 55 phenolic antioxidants based on Conceptual DFT descriptors calculated with the Density Functional Tight Binding (DFTB) GFN1-x?B. Machine learning algorithms were used for feature selection and regression analysis, and Leave-One-Out Cross-Validation was used for both multiple linear regression (MLR) and sequential minimal optimization regression (SMOreg). For ABTS activity, two models were obtained with a correlation coefficient of 0.94 (MLR) and 0.92 (SMOreg). For DPPH activity, two models were obtained with a Correlation Coefficient of 0.93 (MLR) and 0.91 (SMOreg). The number of phenolic groups in the molecule, Bond Dissociation Enthalpy and radical Fukui of .. View More»
  DOI: 10.35248/2157-7609.23.14.286

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