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In this work, the application of a new strategy called NWFE ensemble (nonparametric weighted feature extraction ensemble) method is proposed. Subspace-supervised projections based on NWFE are incorporated into the construction of ensembles of classifiers to facilitate the correct classification of wrongly classified instances without being detrimental to the overall performance of the ensemble. The performance of NWFE is investigated with a c-Jun N-terminal kinase-3 inhibitor benchmark dataset using different chemical compound representation models. Compared with the standard method, the results obtained show that the applied method improves the prediction performance using two classifiers based on decision trees and support vector machines. Image 1 • The new nonparametric weighted feature extraction (NWFE) method is proposed. • It improves classifier performance compared to standard ensemble method. • The fast clustering-based feature selection algorithm showed be appropriated to find an optimal subset of descriptors. • The results were validated using different molecular representation, with JNK3 inhibition models as an example. [ABSTRACT FROM AUTHOR]