[en] A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH‚) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a satisfactory performance for the training `R2 “ 0.713 ̆ and test set `Q2ext “ 0.654 ̆, respectively. To gain greater insight on the relevance of the MD contained in the MLP model, sensitivity and principal component analyses were performed. Moreover, structural and mechanistic interpretation was carried out to comprehend the relationship of the variables in the model with the modeled property. The constructed MLP model was employed to predict the radical scavenging ability for a group of coumarin-type compounds. Finally, in order to validate the model’s predictions, an in vitro assay for one of the compounds (4-hydroxycoumarin) was performed, showing a satisfactory proximity between the experimental and predicted pIC50 values.
Disciplines :
Pharmacy, pharmacology & toxicology
Author, co-author :
Goya-Jorge, Elizabeth ; Université de Liège - ULiège > Département de sciences des denrées alimentaires (DDA) > Gestion de la qualité dans la chaîne alimentaire
Rayar, Anita Maria; Conservatoire National des Arts et Métiers > PhD
Barigye, Stephen J; ProtoQSAR SL > PhD
Jorge-Rodríguez, María Elisa; Universidad Central "Marta Abreu" de las Villas > Department of Pharmacy > Professor
Veitía, Maité Sylla-Iyarreta; Conservatoire National des Arts et Métiers > Equipe de Chimie Moléculaire du Laboratoire Génomique, Bioinformatique et Chimie Moléculaire (EA 7528) > Professor
Language :
English
Title :
Development of an in Silico Model of DPPH‚ Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type Compounds
Publication date :
07 June 2016
Journal title :
International Journal of Molecular Sciences
ISSN :
1661-6596
eISSN :
1422-0067
Publisher :
Multidisciplinary Digital Publishing Institute (MDPI), Switzerland
Special issue title :
Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences
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