Citation:
LÜ Wei, XUE Ying, MENG Qing-Wei. Classification Prediction of Inhibitors of H1N1 Neuraminidase by Machine Learning Methods[J]. Acta Physico-Chimica Sinica,
;2013, 29(01): 217-223.
doi:
10.3866/PKU.WHXB201211122
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Influenza is a major respiratory infection associated with significant morbidity in the general population and mortality in elderly and high-risk patients. Research has shown that inhibiting neuraminidase (NA) prevents RNA replication, so NA is an important drug target in the treatment of H1N1 influenza virus. It is becoming increasingly important to screen and predict molecules that have NA inhibitory activity by computational methods. In this work, we explored several machine learning methods (support vector machine (SVM), k-nearest neighbor (k-NN), and C4.5 decision tree (C4.5 DT)) for predicting NA inhibitors (NAIs). These predictive systems were tested using 227 compounds (72 NAIs and 155 non-NAIs), which were significantly more diverse in chemical structure than those used in other studies. A feature selection method was used to improve the accuracy of the predictions and the selection of molecular descriptors responsible for distinguishing between NAIs and non-NAIs. The prediction accuracies were 75.9%-92.6% for all the compounds, 64.3%-78.6% for NAIs, and 77.5%-97.5% for non-NAIs. The SVM method gave the best total accuracy of 92.6% for all of methods. This work suggests that machine learning methods can be useful to predict potential NAIs from unknown sets of compounds and to determine molecular descriptors associated with NAIs.
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