Rapid activity prediction of HIV-1 integrase inhibitors: harnessing docking energetic components for empirical scoring by chemometric and artificial neural network approaches.

Improving performance of scoring functions for drug docking simulations is a challenging task in the modern discovery pipeline. Among various ways to enhance the efficiency of scoring function, tuning of energetic component approach is an attractive option that provides better predictions. Herein we present the first development of rapid and simple tuning models for predicting and scoring inhibitory activity of investigated ligands docked into catalytic core domain structures of HIV-1 integrase (IN) enzyme. We developed the models using all energetic terms obtained from flexible ligand-rigid receptor dockings by AutoDock4, followed by a data analysis using either partial least squares (PLS) or self-organizing maps (SOMs). The models were established using 66 and 64 ligands of mercaptobenzenesulfonamides for the PLS-based and the SOMs-based inhibitory activity predictions, respectively. The models were then evaluated for their predictability quality using closely related test compounds, as well as five different unrelated inhibitor test sets. Weighting constants for each energy term were also optimized, thus customizing the scoring function for this specific target protein. Root-mean-square error (RMSE) values between the predicted and the experimental inhibitory activities were determined to be <1 (i.e. within a magnitude of a single log scale of actual IC50 values). Hence, we propose that, as a pre-functional assay screening step, AutoDock4 docking in combination with these subsequent rapid weighted energy tuning methods via PLS and SOMs analyses is a viable approach to predict the potential inhibitory activity and to discriminate among small drug-like molecules to target a specific protein of interest.