Computer modeling in predicting the bioactivity of human 5-lipoxygenase inhibitors

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• 作者：Mengdi Zhang ; Zhonghua Xia ; Aixia Yan
• 关键词：Human 5 ; lipoxygenase (5 ; LOX) inhibitors ; Structure–activity relationship (SAR) ; Self ; organizing map (SOM) ; Support vector machine (SVM) ; Multilayer perceptron network (MLP)
• 刊名：Molecular Diversity
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：21
• 期：1
• 页码：235-246
• 全文大小：
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Biochemistry, general; Organic Chemistry; Polymer Sciences; Pharmacy;
• 出版者：Springer International Publishing
• ISSN：1573-501X
• 卷排序：21

文摘

5-Lipoxygenase (5-LOX) is a key enzyme in the inflammatory path. Inhibitors of 5-LOX are useful for the treatment of diseases like arthritis, cancer, and asthma. We have collected a dataset including 220 human 5-LOX inhibitors for classification. A self-organizing map (SOM), a support vector machine (SVM), and a multilayer perceptron (MLP) algorithm were used to build models with selected descriptors for classifying 5-LOX inhibitors into active and weakly active ones. MACCS fingerprints were used in this model building process. The accuracy (Q) and Matthews correlation coefficient (MCC) of the best SOM model (Model 1A) were 86.49% and 0.73 on the test set, respectively. The Q and MCC of the best SVM model (Model 2A) were 82.67% and 0.64 on the test set, respectively. The Q and MCC of the best MLP model (Model 3B) were 84.00% and 0.67 on the test set, respectively. In addition, 180 inhibitors with bioactivities measured by fluorescence method were further used for a quantitative prediction. Multiple linear regression (MLR) and SVM algorithms were used to build models to predict the \(\hbox {IC}_{50}\) values. The correlation coefficients (R) of the MLR model (Model Q1) and the SVM model (Model Q2) were 0.72 and 0.74 on the test set, respectively.