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


  • Olaposi Idowu Omotuyi Nagasaki University, Nagasaki Japan
  • Hiroshi Ueda



LPA3, LPA3 antagonists, Mathematical Model, PCA, Molecular descriptors


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.

Author Biographies

Olaposi Idowu Omotuyi, Nagasaki University, Nagasaki Japan

Molecular Pharmacology and Neuroscience

Graduate (Ph.D) Student

Hiroshi Ueda






Original Articles