FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction

Heliyon
Farshid RayhanSwakkhar Shatabda

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...Continue Reading

References

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Dec 24, 2018·Journal of Theoretical Biology·Farshid RayhanSwakkhar Shatabda

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Citations

May 26, 2021·Molecular Diversity·Joel Markus Vaz, S Balaji
Jul 1, 2021·Journal of Computer-aided Molecular Design·Betsabeh TanooriEghbal G Mansoori
Jul 3, 2021·International Journal of Molecular Sciences·Cheng WangBing Wang
Jul 30, 2020·Current Drug Metabolism·Dongrui GaoYongqing Zhang

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Methods Mentioned

BETA
interaction prediction
X-Ray
interaction predictions

Software Mentioned

DASPfind
Decision
MEBoost
FRnet
SVM
ESBoost
Encodeusing
iDTI
T f l o w Library
SuperTarget

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