Quantitative Structure -Activity Relationship (QSAR) analyses have been carried out for a set of 4-anilino-3-quinolinecarbonitriles. Considering simplicity and interpretability, Src kinase-inhibiting activity of these compounds expressed in log units have been modeled by Multiple Linear Regression (MLR) analysis combined with various variable selection approaches, including Forward Selection (FS), Genetic Algorithm (GA), Simulated Annealing (SA), and Enhanced Replacement Method (ERM), based on descriptors generated by E-Dragon software. Performances of these models are rigorously validated by Leave-One-Out Cross-Validation (LOOCV), five-fold Cross-Validation (5-CV), and external validation. The ERM -MLR model is much better than other models, with R 2 ¼ 0.854 and R 2 pred ¼ 0.840. Robustness and predictive ability of this model are prudently evaluated. Moreover, another classification analysis using Fisher Linear Discriminant Analysis (FLDA) and Support Vector Machine (SVM) is also developed with the aim of dissecting the most significant factors that lead to the activity difference between highly active compounds and those not so active. The 5-CV and external validation prediction accuracy reached 95.00 and 93.75% for the SVM-based model, respectively. The resulting models could act as an efficient strategy for estimating the Src-inhibiting activity of novel 4-anilino-3-quinolinecarbonitriles and provide some insight into the structural features related to the biological activity of these compounds.