Study of quantitative structure-activity relationship (QSAR) of diarylaniline analogues as in vitro anti-HIV-1 agents in pharmaceutical interest
Journal Title: Chemical Methodologies - Year 2017, Vol 2, Issue 2
Abstract
A study of quantitative structure-activity relationship (QSAR) is applied to a set of 24 molecules derived from diarylaniline to predict the anti-HIV-1 biological activity of the test compounds and find a correlation between the different physic-chemical parameters (descriptors) of these compounds and its biological activity, using principal components analysis (PCA), multiple linear regression (MLR), multiple non-linear regression (MNLR) and the artificial neural network (ANN). We accordingly proposed a quantitative model (non-linear and linear QSAR models), and we interpreted the activity of the compounds relying on the multivariate statistical analysis. The topological descriptors were computed with ACD/ChemSketch and ChemBioOffice14.0 programs. A correlation was found between the experimental activity and those obtained by MLR and MNLR such as ( Rtrain = 0.886 ; R2train = 0.786 ) and (Rtrain = 0.925 ; R2train = 0.857 ) for the training set compounds, and ( RMLR-test = 0.6 ) and ( RMNLR-test = 0.7 ) for a randomly chosen test set of compounds, this result could be improved with ANN such as ( R = 0.916 and R2 = 0.84 ) with an architecture ANN (6-1-1). To evaluate the performance of the neural network and the validity of our choice of descriptors selected by MLR and trained by MNLR and ANN, we used cross-validation method (CV) including ( R = 0.903 and R2 = 0.815 ) with the procedure leave-one-out (LOO). The results showed that the MLR and MNLR have served to predict activities, but when compared with the results given by an 6-1-1 ANN model. We realized that the predictions fulfilled by the latter model were more effective than the other models. The statistical results indicated that this model is statistically significant and showing a very good stability towards the data variation in leave-one-out (LOO) cross validation.
Authors and Affiliations
Mohammed Bouachrin, Youness Bouakarai, Fouad Khalil
Keywords
Related Articles
- EP ID EP252725
- DOI 10.22631/chemm.2017.101407.1016
- Views 84
- Downloads 0
Cu Nanoparticle: Synthesis, Characterization and Application
The applications of copper nanoparticle are gradually increased because of Cu is inexpensiveness and high abundance in nature. However, synthesis of copper nanoparticles is very challenging because of transformation from...
In Silico Study of Metoclopramide as A Small Molecule of Dopamine D2 Receptor: a Quantum-Mechanical (QM) Based Molecular Docking Treatment
The present research exploration will contain studying the molecular structure, bonds nature, stability, reactivity and electronic properties of the title molecule.The molecular optimization and all theoretical computati...
Prediction of Atmospheric Corrosion of Ancient Door Knockers via Neural Networks
The importance of door knockers persuades us to anticipate the atmospheric corrosion through Neural Network (NN) which is validated by data originated from literature. NNs are used in order to anticipate the effective pa...
Approach to chemometrics models by artificial neural network for structure: first applications for estimation retention time of doping agent
A quantitative structure–retention relationship (QSRR), was developed using the genetic algorithm-partial least square (GA-PLS), Kernel partial least square (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-...
N1,N1,N2,N2-Tetramethylethylenediaminium-N1,N2-disulfonic Acid Trifluoroacetate as Highly Effectual and Dual-Functional Catalyst for the Reaction of β-Ketoesters with Aryl Aldehydes and Urea/Thiourea
In this research, the one-pot multi-component reaction of β-ketoesters with aryl aldehydes and urea/thiourea has been performed using a dual-functional ionic liquid-catalyst namely N1,N1,N2,N2-tetramethylethylenediaminiu...