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.