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

• 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

 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 between drug and target in the bioinformatics network composed of drugs, proteins and other related data. Results: In this work, we have developed a heterogeneous graph neural network model, named as HGDTI, which includes a learning phase of network node embedding and a training phase of DTI classification. This method first obtains the molecular fingerprint information of drugs and the pseudo amino acid composition information of proteins, then extracts the initial features of nodes through Bi-LSTM, and uses the attention mechanism to aggregate heterogeneous neighbors. In several comparative experiments, the overall performance of HGDTI significantly outperforms other state-of-the-art DTI prediction models, and the negative sampling technology is employed to further optimize the prediction power of model. In addition, we have proved the robustness of HGDTI through heterogeneous network content reduction tests, and proved the rationality of HGDTI through other comparative experiments. These results indicate that HGDTI can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. Conclusions: The HGDTI based on heterogeneous graph neural network model, can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. For the convenience of related researchers, a user-friendly web-server has been established at http://bioinfo.jcu.edu.cn/hgdti. © 2022, The Author(s).