Abstract
BackgroundDrug-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.ResultsWe 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.ConclusionsAll 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.
Highlights
Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and is a vital step in drug discovery
Drug-target interactions (DTIs) prediction is of great significance for drug repositioning [2], drug discovery [3], side-effect prediction [4] and drug resistance [5]
The ligand-based approaches are very effective in DTIs prediction, but it often requires a large number of known binding data and the prediction results are poor with only a small amount of known data [11]
Summary
Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and is a vital step in drug discovery. 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. Drug-target interactions (DTIs) prediction is of great significance for drug repositioning [2], drug discovery [3], side-effect prediction [4] and drug resistance [5]. Identifying the drug-target interactions via biochemical and chemical biological experiments is costly and time-consuming [6]. As genomic, chemical, and pharmacological data become more and more complete, new opportunities for identifying drug target interactions have been emerged [2]. Many researchers have attempted to predict DTIs by using silico or computational approaches to guide in vivo validation in recent years, and significantly reduce the cost and time for identifying the drug-target interactions [2]. The ligand-based approaches are very effective in DTIs prediction, but it often requires a large number of known binding data and the prediction results are poor with only a small amount of known data [11]
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