DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network.
J Mol Graph Model
; 130: 108783, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38677034
ABSTRACT
Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Reposicionamento de Medicamentos
Limite:
Humans
Idioma:
En
Revista:
J Mol Graph Model
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China