HGDTI: predicting drug–target interaction by using information aggregation based on heterogeneous graph neural network

HGDTI: predicting drug–target interaction by using information aggregation based on heterogeneous graph neural network

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Background: In research on new drug discovery, the traditional wet experiment has a long period. Predicting drug–target interaction (DTI) in silico can greatly narrow the scope of search of candidate medications. Excellent algorithm model may be more effective in revealing the potential connection b...

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Bibliographic Details
Main Authors: Cheng, X. (Author), Dai, J. (Author), Lin, W. (Author), Qiu, W. (Author), Xiao, X. (Author), Yu, L. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
algorithm
Algorithms
Amino acids
biology
Comparative experiments
Computational Biology
drug development
Drug development
Drug Development
drug interaction
Drug interactions
Drug Interactions
Drug–target interaction
Drug-target interactions
Embeddings
Forecasting
Graph neural network
Graph neural networks
Heterogeneous graph
Heterogeneous information
Heterogeneous networks
Long short-term memory
metabolism
Molecular fingerprint
Network embeddings
Neural network model
Neural Networks, Computer
procedures
protein
Proteins
Pseudo amino acid composition
Pseudo Amino Acid Compositions
Tensors
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