QSAR Models for isoindolinone-based p53-MDM2 Interaction Inhibitors Using Linear and Non-linear Statistical Methods

Abstract

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 (R train2=0.881, Q loo2=0.847, R test2=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: R train2=0.914, Q loo2=0.894 and R test2=0.903; SVMR: R train2=0.924, Q loo2=0.920 and R test2 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. © 2012 John Wiley & Sons A/S.