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:
Amino acids
Comparative experiments
Drug development
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
Molecular fingerprint
Network embeddings
Neural network model
Proteins
Pseudo amino acid composition
Pseudo Amino Acid Compositions
Tensors
Online Access:View Fulltext in Publisher

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