Automatic Selection of Molecular Descriptors using Random Forest: Application to Drug Discovery

Cano, G., Garcia-Rodriguez, J., Garcia-Garcia, A, Perez-Sanchez, H., Benediktsson, J.A., Thapa, A. and Barr, A.J. (2017) Automatic Selection of Molecular Descriptors using Random Forest: Application to Drug Discovery. Expert Systems with Applications, 72. pp. 151-159. ISSN 0957-4174

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The optimal selection of chemical features (molecular descriptors) is an essential pre-processing step for the efficient application of computational intelligence techniques in virtual screening for identification of bioactive molecules in drug discovery. The selection of molecular descriptors has key influence in the accuracy of affinity prediction. In order to improve this prediction, we examined a Random Forest (RF)-based approach to automatically select molecular descriptors of training data for ligands of kinases, nuclear hormone receptors, and other enzymes. The reduction of features to use during prediction dramatically reduces the computing time over existing approaches and consequently permits the exploration of much larger sets of experimental data. To test the validity of the method, we compared the results of our approach with the ones obtained using manual feature selection in our previous study (Perez-Sanchez et al., 2014). The main novelty of this work in the field of drug discovery is the use of RF in two different ways: feature ranking and dimensionality reduction, and classification using the automatically selected feature subset. Our RF-based method out-performs classification results provided by Support Vector Machine (SVM) and Neural Networks (NN) approaches.

Item Type: Article
Uncontrolled Keywords: Random Forest, Drug Discovery, Molecular Descriptors, Computational Chemistry;
Subjects: University of Westminster > Science and Technology
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Date Deposited: 15 Dec 2016 13:40
Last Modified: 06 Dec 2017 23:02

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