Classification models for predicting the antimalarial activity against Plasmodium falciparum. Issue 4 (2nd April 2020)

• Record Type: Journal Article
• Authors: Liu, Q.; Deng, J.; Liu, M.
• Is Part Of: SAR and QSAR in environmental research. Volume 31:Issue 4(2020)
• Page Start: 313
• Page End: 324
• Publication Date: 2020-04-02
• Subjects: Antimalarial -- binary logistic regression -- classification model -- general regression neural network -- support vector classification; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2020.1740890
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: Support vector machine (SVM) and general regression neural network (GRNN) were used to develop classification models for predicting the antimalarial activity against Plasmodium falciparum . Only 15 molecular descriptors were used to build the classification models for the antimalarial activities of 4750 compounds, which were divided into a training set (3887 compounds) and a test set (863 compounds). For the SVM model, its prediction accuracies are 89.5% for the training set and 87.3% for the test set. For the GRNN model, the prediction accuracies for the two sets are 99.7% and 88.9%, respectively. Both SVC and GRNN models have better prediction ability than the classification model based on binary logistic regression (BLR) analysis. Compared with previously published classification models both SVC and GRNN models are satisfactory in predicting antimalarial activities of compounds with in addition of fewer descriptors.