Structure Based Activity Prediction of HIV-1 Reverse Transcriptase Inhibitors

Abstract

We have developed a fast and robust computational method for prediction of antiviral activity in automated de novo design of HIV-1 reverse transcriptase inhibitors. This is a structure-based approach that uses a linear relation between activity and interaction energy with discrete orientation sampling and with localized interaction energy terms. The localization allows for the analysis of mutations of the protein target and for the separation of inhibition and a specific binding to the enzyme. We apply the method to the prediction of pIC₅₀ of HIV-1 reverse transcriptase inhibitors. The model predicts the activity of an arbitrary compound with a q² of 0.681 and an average absolute error of 0.66 log value, and it is fast enough to be used in high-throughput computational applications.