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Targeted drugs have been applied to the treatment of cancer on a large scale, and some patients have certain therapeutic effects. It is a time-consuming task to detect drug–target interactions (DTIs) through biochemical experiments. At present, machine learning (ML) has been widely applied in large-scale drug screening. However, there are few methods for multiple information fusion. We propose a multiple kernel-based triple collaborative matrix factorization (MK-TCMF) method to predict DTIs. The multiple kernel matrices (contain chemical, biological and clinical information) are integrated via multi-kernel learning (MKL) algorithm. And the original adjacency matrix of DTIs could be decomposed into three matrices, including the latent feature matrix of the drug space, latent feature matrix of the target space and the bi-projection matrix (used to join the two feature spaces). To obtain better prediction performance, MKL algorithm can regulate the weight of each kernel matrix according to the prediction error. The weights of drug side-effects and target sequence are the highest. Compared with other computational methods, our model has better performance on four test data sets. [ABSTRACT FROM AUTHOR]

Detection of Drug–Target Interactions (DTIs) is the time-consuming and laborious experiment via biochemical approaches. Machine learning based methods have been widely used to mine meaningful information of drug research. In this study, we establish a novel computational method to predict DTIs via Dual Laplacian Regularized Least Squares model (DLapRLS) with Hilbert–Schmidt Independence Criterion-based Multiple Kernel Learning (HSIC-MKL). Multiple kernels are built from different information sources (drug and target spaces). Then, above corresponding kernels are integrated by HSIC-MKL. At last, DLapRLS model is trained by Alternating Least Squares Algorithm (ALSA) and employed to predict new DTIs. On four benchmark datasets, the results of our method are comparable and even better than existing models. • DLapRLS employs alternating least squares algorithm to solve the final model. • Heterogeneous information (kernels) is integrated via multiple kernel learning. • For HSIC-MKL, we employ the Laplacian regular term to smooth the weights. [ABSTRACT FROM AUTHOR]