The design and optimization of p53–MDM2 interaction inhibitors has attracted a great deal of interest in the development of new anticancer agents. Systematical 2D-QSAR studies on 98 isoindolinone-based p53–MDM2 interaction inhibitors were carried out using linear and the non-linear mathematical methods. At first, a forward stepwise-multiple linear regression model (FS-MLR) was proposed with reasonable statistical parameters (Rtrain2 = 0.881, Qloo2 = 0.847, Rtest2 = 0.854). Then, enhanced replacement method–multiple linear regression (ERM-MLR) and support vector machine regression (SVMR) were applied to set up more accurate models (ERM-MLR: Rtrain2 = 0.914, Qloo2 = 0.894 and Rtest2 = 0.903; SVMR: Rtrain2 = 0.924, Qloo2 = 0.920 and Rtest2 of 0.874). Furthermore, the reliability and application value of the ERM and SVMR model was also validated in virtual screening through receiver operating characteristic studies.