Acta Phys. -Chim. Sin. ›› 2013, Vol. 29 ›› Issue (01): 217-223.doi: 10.3866/PKU.WHXB201211122

• BIOPHYSICAL CHEMISTRY • Previous Articles     Next Articles

Classification Prediction of Inhibitors of H1N1 Neuraminidase by Machine Learning Methods

LÜ Wei1,2, XUE Ying3,4, MENG Qing-Wei1,2   

  1. 1 College of Life Sciences, State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an, Shandong 271018, P. R. China;
    2 Postdoctoral Research Bachelor of Biology, Shandong Agricultural University, Tai’an, Shandong 271018, P. R. China;
    3 College of Chemistry, Key Laboratory of Green Chemistry and Technology, Ministry of Education, Sichuan University, Chengdu 610064, P. R. China;
    4 State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, P. R. China
  • Received:2012-09-13 Revised:2012-11-12 Published:2012-12-14
  • Supported by:

    The project was supported by the National Key Basic Research Program of China (973) (2009CB118500).

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Abstract:

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.

Key words: Machine learning method, H1N1 influenza virus, Neuraminidase inhibitor, Support vector machine