Abstract

BACKGROUND: Human immunodeficiency virus type 1 (HIV-1) is the causative agent of AIDS occurs across mucosal surfaces or by direct inoculation. OBJECTIVE: The objective of this study was to consider chemically diverse scaffold sets of HIV-1 Reverse Transcriptase Inhibitors (HIV-1 RTI) subjected to ideal oriented QSAR with large descriptor space. METHOD: We generated a four-parameter QSAR model based on 111 data points, which provided an optimum prediction of HIV-1 RTI for overall 367 experimentally measured compounds. RESULTS: The robustness of the model is demonstrated by its statistical validation (Ntraining = 111, R2 = 0.85, Q2lmo = 0.84) and by the prediction of HIV-1 inhibition activity for experimentally measured compounds. CONCLUSION: Finally, 5 novel hit compounds were designed in silico by using a virtual screening approach. The new hits met all the pharmacophore constraints and predicted pIC50 values within the binding ability of HIV-1 RT protein targets.

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