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.

使用支持向量机（SVM）和通用回归神经网络（GRNN）开发分类模型，以预测对恶性疟原虫的抗疟活性。仅使用15个分子描述子来建立4750种化合物的抗疟活性的分类模型，将其分为训练集（3887个化合物）和测试集（863个化合物）。对于SVM模型，训练集的预测准确性为89.5％，测试集的预测准确性为87.3％。对于GRNN模型，两组的预测准确度分别为99.7％和88.9％。与基于二进制逻辑回归（BLR）分析的分类模型相比，SVC和GRNN模型均具有更好的预测能力。与以前发布的分类模型相比，SVC和GRNN模型在预测化合物的抗疟疾活性方面令人满意，并且描述符更少。