Chemometric QSAR Modeling and In Silico Design of Antioxidant NO Donor Phenols

Abstract

An acceleration of free radical formation within human system exacerbates the incidence of several life-threatening diseases. The systemic antioxidants often fall short for neutralizing the free radicals thereby demanding external antioxidant supplementation. Therein arises the need for development of new antioxidants with improved potency. In order to search for efficient antioxidant molecules, the present work deals with quantitative structure-activity relationship (QSAR) studies of a series of antioxidants belonging to the class of phenolic derivatives bearing NO donor groups. In this study, several QSAR models with appreciable statistical significance have been reported. Models were built using various chemometric tools and validated both internally and externally. These models chiefly infer that presence of substituted aromatic carbons, long chain branched substituents, an oxadiazole-N-oxide ring with an electronegative atom containing group substituted at the 5 position and high degree of methyl substitutions of the parent moiety are conducive to the antioxidant activity profile of these molecules. The novelty of this work is not only that the structural attributes of NO donor phenolic compounds required for potent antioxidant activity have been explored in this study, but new compounds with possible antioxidant activity have also been designed and their antioxidant activity has been predicted in silico.

In silico design of hydrazone antioxidants and analysis of their free radical-scavenging mechanism by thermodynamic studies

“…This was performed by insertion, deletion and substitution of different substitutes on the template molecule as dictated by the developed hydrazone antioxidant model [6,25,28]. In the present research, compound M010 ( Fig.…”

Artificial neural network-based equation to predict the toxicity of herbicides on rats

“…Thus, the ( ) 2 statistical may be used, since it is based on both test set and training set predictions. Therefore, the result is based on the prediction of a comparably large number of compounds [53]. Squared correlation coefficient between the observed and predicted value of compounds without intercept.…”

QSPR studije karbonilnih, hidroksilnih, polienskih indeksa i prosječne molekulske težine polimera pod fotostabilizacijom pristupom ANN i MLR

“…The statistical validation of models consists of internal and external validations. Recent studies [44][45][46][47][48] have indicated that internal validation is essential for the validation of a QSPR/QSAR model. In our study, the most important traditional validation metrics were applied: root mean square error (RMSE), determination coefficient (R 2 ), cross validated correlation coefficient (Q 2 LOO ), in addition to the use of the parameters ( 2 m r ;…”