Linear and Non-linear Quantitative Structure – Activity Relationship Models on Indole Substitution Patterns as Inhibitors of HIV-1 Attachment

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This study was performed to develop a quantitative structure–activity relationship (QSAR) model of the biological activity of indole glyoxamide derivatives as an inhibitor of the interaction between human immunodeficiency virus (HIV) glycoprotein gp120 and host cell CD4 receptors. In present study, forty different compounds were selected as a sample set. Combinations of multiple linear regressions (MLR), genetic algorithms (GA) and artificial neural networks (ANN) were then utilized to construct the QSAR models. These models were also utilized to nonlinearly select the most efficient subsets of descriptors in a cross-validation procedure for nonlinear log (1/EC50) prediction. The obtained results using GA-ANN were compared with MLR-MLR and MLR-ANN models. The obtained models showed high prediction ability with root mean sum square error (RMSE) of 0.99, 0.91 and 0.67 for MLR, MLR-ANN and GA-ANN models, respectively (N=40).

Keywords: Genetic algorithm; artificial neural network; multiple linear regressions; HIV;

Quantitative Structure – Activity Relationship;

1. Introduction

The process of human immunodeficiency virus-1( HIV-1) entry into host cells, offers considerable potential for therapeutic intervention, with viral entry proceeding through multiple sequential steps involving attachment, coreceptor binding, and fusion (8, 13). The early step of viral entry into the host cell is accomplished through binding of the viral envelope glycoprotein complex gp160 to the cellular receptor, CD4. This attachment is followed by conformational changes of the gp160 external glycoprotein portion, gp120, which facilitates the second step involving binding to a cellular co-receptor, usually the chemokine recep...

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