Mer receptor tyrosine kinase is a promising novel cancer therapeutic target in many human cancers, because abnormal activation of Mer has been implicated in survival signaling and chemoresistance. 3D-QSAR analyses based on alignment independent descriptors were performed on a series of 81 Mer specific tyrosine kinase inhibitors. The fractional factorial design (FFD) and the enhanced replacement method (ERM) were applied and tested as variable selection algorithms for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. The data set was split into 65 molecules as the training set and 16 compounds as the test set. All descriptors were generated by using the GRid INdependent descriptors (GRIND) approach. After variable selection, GRIND were correlated with activity values (pIC50) by PLS regression. Of the two applied variable selection methods, ERM had a noticeable improvement on the statistical parameters of PLS model, and yielded a q2 value of 0.77, an rpred2 of 0.94, and a low RMSEP value of 0.25. The GRIND information contents influencing the affinity on Mer specific tyrosine kinase were also confirmed by docking studies. In a quantum calculation study, the energy difference between HOMO and LUMO (gap) implied the high interaction of the most active molecule in the active site of the protein. In addition, the molecular electrostatic potential energy at DFT level confirmed results obtained from the molecular docking. The identified key features obtained from the molecular modeling, enabled us to design novel kinase inhibitors.