Sparse feature selection in multi-target modeling of carbonic anhydrase isoforms by exploiting shared information among multiple targets

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Multi-target modeling can be used for inhibition prediction of CA isoforms, the essential zinc metalloenzymes involved in different biological processes such as tumorigenesis. In this study, the first multi-target model is developed for predicting the activities of inhibitors against CA-I, CA-II, CA-IX, and CA-XII. Structural similarity analysis is carried out for two cancer-related isoforms CA-IX and CA-XII. The mean TM-score value (0.935) reveals a marked similarity between the two structures. To select relevant descriptors for the developed multi-target model, we propose a novel feature selection method based on shared subspace learning, which considers correlation among different targets in multi-target modeling. The proposed shared subspace feature selection method uses the mixed convex and non-convex l2,p-norm (0 ​< ​p ​≤ ​1) minimization on both regularization and loss function to ensure that the loss function is robust to outliers and consider correlation among different descriptors for joint sparse feature selection. To solve the proposed shared subspace feature selection method for convex and non-convex cases, a unified Algorithm is presented. The study utilized a test set to evaluate the performance of the proposed feature selection method with a multi-target kernel smoother model and compare to that of other feature selection methods with the multi-target kernel smoother models. The obtained results demonstrate the superiority of the proposed shared-subspace feature selection approach based on l2,1/2-norm in selecting the most relevant descriptors. Statistical results (RMSEtest ​= ​0.5190, R2test ​= ​0.7613 and Q2ext ​= ​0.7524) demonstrate that the model displays adequate quality for virtual screening. The results also represent the significance of using the shared subspace among different targets in the selection of relevant descriptors to predict the inhibition of CA isoforms.