NeuRank: learning to rank with neural networks for drug–target interaction prediction

NeuRank: learning to rank with neural networks for drug–target interaction prediction

Background: Experimental verification of a drug discovery process is expensive and time-consuming. Therefore, recently, the demand to more efficiently and effectively identify drug–target interactions (DTIs) has intensified. Results: We treat the prediction of DTIs as a ranking problem and propose a...

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Bibliographic Details
Main Authors: Lin, F. (Author), Wu, X. (Author), Zeng, W. (Author), Zhou, X. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
cell receptor
drug
Drug compounds
drug development
Drug Development
Drug discovery
Drug Discovery
Drug discovery process
Drug interactions
Drug-target interactions
Drug–target interactions
Experimental verification
Forecasting
Interaction prediction
Network architecture
Neural network
Neural network architecture
Neural Networks, Computer
Neural-networks
Pharmaceutical Preparations
Proteins
Ranking problems
Ranking task
Receptors, Cytoplasmic and Nuclear
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