Classification study of novel piperazines as antagonists for the melanocortin-4 receptor based on least-squares support vector machines

The least-squares support vector machine (LS-SVM), as an effective machine learning algorithm, was used to develop a nonlinear binary classification model of novel piperazines-bis- piperazines as antagonists for the melanocortin-4 (MC4) receptor based on their activity. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, quantum-chemical features. Five descriptors selected by forward stepwise linear discriminant analysis (LDA) were used as inputs of the LS-SVM model. The nonlinear model developed from LS-SVM algorithm (with prediction accuracy of 95% on the test set) outperformed LDA (test accuracy of 90%). The proposed method is very useful for chemists to screen antagonists for the MC4 receptor.