A learning based method for drug target interaction prediction based on feature representation learning and deep neural network
Background Drug target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time. Results We propose a learning based method based on feature representation learning and deep neural network named DTI CNN to predict the drug target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state of the art methods. Conclusions All the experimental results show that the performance of DTI CNN is better than that of the three existing methods and the proposed method is appropriately designed.
BMC Bioinform.