A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural
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RESEARCH
Open Access
A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network Jiajie Peng1,2 , Jingyi Li1,2 and Xuequn Shang1,2* From The 18th Asia Pacific Bioinformatics Conference Seoul, Korea. 18-20 August 2020 *Correspondence: [email protected] 1 The School of Computer Science, Northwestern Polytechnical University, 710072 Xian, China 2 The Key Laboratory of Big Data Storage an Management, Northwestern Polytechnical Universitythe, Ministry of Industry and Information Technology, 710072 Xian, China
Abstract 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. Keywords: DTIs prediction, Convolutional neural network, Feature representation learning
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