A Classification Study of Respiratory Syncytial Virus (RSV) Inhibitors by Variable Selection with Random Forest

Int. J. Mol. Sci. 2011, 12(2), 1259-1280; doi:10.3390/ijms12021259

Author

Ming Hao ¹

, Yan Li ^1,* , Yonghua Wang ²

 and Shuwei Zhang ¹

¹

School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China

²

Center of Bioinformatics, Northwest A&F University, Yangling, Shaanxi 712100, China

^*

Author to whom correspondence should be addressed. 

Received: 13 December 2010 / Revised: 10 February 2011 / Accepted: 11 February 2011 / Published: 21 February 2011

(This article belongs to the Section Biochemistry, Molecular Biology and Biophysics)

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Abstract 

Experimental pEC₅₀s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development. View Full-Text

Keywords: RSV;  variable selection;  Mold² descriptors;  random forest

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