Abstract
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/.
Highlights
The task of drug-target interaction prediction is very important in pharmacology and therapeutic drug design
Note that other classifiers achieved very impressive area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) score which shows the effectiveness of the features generated by FRnet-Encode
For each of the datasets except the nuclear receptor (NR) dataset, performance of FRnet-Predict is superior to the other methods both in terms of auPR and auROC
Summary
The task of drug-target interaction prediction is very important in pharmacology and therapeutic drug design. This problem can be addressed in several ways. For an already developed drug compound the task is to find new targets with which the drug might have interactions. For a given target protein one might search for potential drugs in the library. Another way to tackle the problem is to find the possibility of interaction given a pair of drug and target protein. Experimental methods in predicting drug-protein interactions are expensive and time consuming and computational methods have been used extensively in the recent years [1, 2]
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