SMILES‐based optimal descriptors: QSAR analysis of fullerene‐based HIV‐1 PR inhibitors by means of balance of correlations

Abstract

Quantitative structure-activity relationships (QSAR) for prediction of binding affinities (pEC50, i.e., minus decimal logarithm of the 50% effective concentration) of 20 fullerene derivatives inhibitors of the HIV-1 PR (human immunodeficiency virus type 1 protease) have been developed by application of the optimal descriptors approach calculated with SMILES (simplified molecular input line entry system). The applied models were constructed by the balance of correlations. Three various splits of the experimental data into subtraining set, calibration set, and test set were examined. Comparison of classic scheme (training-test system) and the balance of correlations (subtraining-calibration-test system) show that the balance of correlations gives more robust predictions than the classic scheme for the pEC50 of the fullerene derivatives.