A classification study of respiratory Syncytial Virus (RSV) inhibitors by variable selection with random forest

Abstract

Experimental pEC(50)s 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.

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

Figure 1.

Self-organizing map (SOM) top map indicating the distribution of the training and external prediction sets. The training set is labeled in black font and the prediction set in red font. The number corresponds to the series number of the compounds of the RSV inhibitors.

Figure 2.

The ROC (receiver operating characteristic) curves of VS-RF, SVM, GP, LDA and kNN for the prediction set.