Girinath G. Pillai, Laznier Mederos, Chandramukhi S. Panda, Amber Gronski, Peeter Burk, Charles D. Hall, Alan R. Katritzky, Kaido Tämm and Mati Karelson Pages 513 - 526 ( 14 )
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.
Global QSAR, applicability domain, diverse scaffolds, field points, scaffold hopping.
Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia.