Descriptor-based Fitting of LPA3 Inhibitors into a Single Predictive Mathematical Model

Abstract

Sixty six diverse compounds previously reported as Lysophosphatidic Acid Receptor (LPA3) inhibitors have been used to derive a mathematical model based on partial least square (PLS) clustering of 41 molecular descriptors and pIC50 values. The pre- and post- cross-validated correlation coefficient (R2) is 0.94462 (RMSE=0:21390) and 0.74745 (RMSE=0.49055) respectively. Bivariate contingency analysis tools implemented in MOE was used to prune the descriptors and refit the equations at a descriptor-pIC50 correlation coefficient of 0.8 cutoff. A new equation was derived with R2 and RMSE values estimated at 0.88074 and 0.31388 respectively. Both equations correctly predicted the 95% of the pIC50 values of the test dataset. Principal component analysis (PCA) was also used to reduce the dimension and linearly transform the raw data; 8 principal components sufficiently account for more than 98% of the variance of the dataset. The numerical model derived here may be adapted for screening chemical database for LPA3 antagonism.