  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 and a convolutional classifier for feature
manipulation and drug target interaction prediction. Two convolutional neural
neworks are proposed where one model is used for feature manipulation and the
other one for classification. Using the first method FRnet-1, we generate 4096
features for each of the instances in each of the datasets and use the second
method, FRnet-2, to identify interaction probability employing those features.
We have tested our method on four gold standard datasets exhaustively used by
other researchers. Experimental results shows that our method significantly
improves over the state-of-the-art method on three of the 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. Codes Available: https: // github.
com/ farshidrayhanuiu/ FRnet-DTI/ Web Implementation: http: // farshidrayhan.
pythonanywhere. com/ FRnet-DTI/
