2D binary QSAR modeling of LPA3 receptor antagonism

Abstract

A structurally diverse dataset of 119 compounds was used to develop and validate a 2D binary QSAR model for the LPA(3) receptor. The binary QSAR model was generated using an activity threshold of greater than 15% inhibition at 10 microM. The overall accuracy of the model on the training set was 82%. It had accuracies of 55% for active and 91% for inactive compounds, respectively. The model was validated using an external test set of 10 compounds. The accuracy on the external test set was 60% overall, identifying three out of seven actives and all three inactive compounds. This model was combined with similarity searching to rapidly screen libraries and select 14 candidate LPA(3) antagonists. Experimental assays confirmed 13 of these (93%) met the 15% inhibition threshold defining actives. The successful application of the model to select candidates for screening demonstrates the power of this binary QSAR model to prioritize compound selection for experimental consideration.