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. 2016. Automatic Selection of Molecular Descriptors using Random Forest: Application to Drug Discovery. Expert Systems with Applications. 72, pp. 151-159.

TitleAutomatic Selection of Molecular Descriptors using Random Forest: Application to Drug Discovery
AuthorsCano, G., Garcia-Rodriguez, J., Garcia-Garcia, A, Perez-Sanchez, H., Benediktsson, J.A., Thapa, A. and Barr, A.J.
Abstract

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.

KeywordsRandom Forest, Drug Discovery, Molecular Descriptors, Computational Chemistry
JournalExpert Systems with Applications
Journal citation72, pp. 151-159
ISSN0957-4174
Year2016
PublisherElsevier
Accepted author manuscriptESWA-D-16-02533R2-1 (1)-1.pdf
Digital Object Identifier (DOI)doi:10.1016/j.eswa.2016.12.008
Publication dates
Published online06 Dec 2016
Published06 Dec 2016
Published in print15 Apr 2017

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