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

摘要

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 95percent on the test set) outperformed LDA (test accuracy of 90percent). The proposed method is very useful for chemists to screen antagonists for the MC4 receptor.