A priori molecular descriptors in QSAR: a case of HIV-1 protease inhibitors. I. The chemometric approach.

A quantitative structure-activity relationship (QSAR) study on 48 peptidic HIV-1 protease inhibitors was performed. Fourteen a priori molecular descriptors were used to build QSAR models. Hierarchical cluster analysis (HCA), principal component analysis (PCA) and partial least squares (PLS) regression were employed. PLS models with 32/16 (model I) and 48/0 (model II) molecules in the training/external validation set were constructed. The a priori molecular descriptors were related to two energetic variables using PLS. HCA and PCA on data from model II classified the inhibitors as slightly, moderately and highly active; three principal components, the chemical nature of which has been highlighted, are enough to describe the enzyme-inhibitor binding. Model I (r(2)=0.91, q(2)=0.84) is comparable to literature models obtained by various QSAR softwares, which justified the use of a priori descriptors.

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