MIA-QSAR, PCA-ranking and least-squares support-vector machines in the accurate prediction of the activities of phosphodiesterase type 5 (PDE-5) inhibitors

Abstract

Phosphodiesterase type-5 (PDE-5) is a key enzyme involved in the erection process. PDE-5 inhibitors, such as Sildenafil (ViagraTM), Vardenafil (LevitraTM) and Tadalafil (CialisTM), are used for the treatment of erectile dysfunction. Computer-assisted modelling of biological activities of PDE-5 inhibitors may make quantitative structure–activity relationship (QSAR) models useful for the development of safer (low side effects) and more potent drugs. The multivariate image analysis applied to QSAR (MIA-QSAR) method, coupled to partial least-squares (PLS) regression, has provided highly predictive QSAR models. Nevertheless, regression methods which take into account nonlinearity, such as least-squares support-vector machines (LS-SVMs), are supposed to predict biological activities more accurately than the usual linear methods. Thus, together with prior variable selection using principal component analysis ranking, MIA-QSAR and LS-SVM regression were applied to model the bioactivities of a series of cyclic guanine derivatives (PDE-5 inhibitors), and the results were compared with those based on linear methodologies. MIA-QSAR/LS-SVM was found to improve greatly the prediction performance when compared with MIA-QSAR/PLS, MIA-QSAR/N-PLS, CoMFA/PLS and CoMSIA/PLS models.