QSAR analysis for some β-carboline derivatives as anti-tumor

Quantitative Structure Activity/Pharmacokinetics Relationship Studies of HIV-1 Protease Inhibitors Using Three Modelling Methods

BACKGROUND

HIV-1 protease inhibitor (PIs) is a good choice for AIDS patients. Nevertheless, for PIs, there are several bugs in clinical application, like drug resistance, the large dose, the high costs and so on, among which, the poor pharmacokinetics property is one of the important reasons that leads to the failure of its clinical application.

OBJECTIVE

We aimed to build computational models for studying the relationship between PIs structure and its pharmacological activities.

METHODS

We collected experimental values of k_off/K_i and structures of 50 PIs through a careful literature and database search. Quantitative structure activity/pharmacokinetics relationship (QSAR/QSPR) models were constructed by support vector machine (SVM), partial-least squares regression (PLSR) and back-propagation neural network (BPNN).

RESULTS

For QSAR models, SVM, PLSR and BPNN all generated reliable prediction models with the r² of 0.688, 0.768 and 0.787, respectively, and r²_pred of 0.748, 0.696 and 0.640, respectively. For QSPR models, the optimum models of SVM, PLSR and BPNN obtained the r² of 0.952, 0.869 and 0.960, respectively, and the r²_pred of 0.852, 0.628 and 0.814, respectively.

CONCLUSION

Among these three modelling methods, SVM showed superior ability than PLSR and BPNN both in QSAR/QSPR modelling of PIs, thus, we suspected that SVM was more suitable for predicting activities of PIs. In addition, 3D-MoRSE descriptors may have a tight relationship with the Ki values of PIs, and the GETAWAY descriptors have significant influence on both koff and Ki in PLSR equations.