Abstract

Y. Belmiloud¹, A. Kadari¹, L. Benahmed², D. Cherqaoui³, D. Villemin⁴ and M. Brahimi¹

Artificial neural network (NN) was constructed and trained for the prediction of the anti- human immunodeficiency virus (anti-HIV) activity for 26 flavonoîd compounds based on quantitative structureactivity relationship method (QSAR). For different models, The network, inputs were selected by the stepwise multiple linear regressions technique (MLR) by using Codessa program.NN based obtained results lead to statistical results in good agreement with the literature data. They put in evidence the importance of the molecular hydrophobicity, electronegativity and atomic charges on some key atoms in modelling flavonoid compounds' behaviour by means of QSAR approach. Nonlinear NN models are shown to give better results with good predictive anti-HIV activity than linear ones.

Flavonoid; multiple linear regressions technique MLR; quantitative structure-activity relationship method QSAR; neural network NN; anti-HIV; DFT

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