SGCL-DTI:

Supervised Co-contrastive Graph Learning for Drug-Target Interaction Prediction

Quick start

We provide an example script to run experiments on our dataset:

• Run ./SGCL-DTI/main.py: predict drug-target interactions.

All process

-Run ./main.py You can run the entire model

Code and data

• CLaugmentdti.py: data augment for graph contrastive learning
• modeltestdtiseed.py: SGCL model
• utilsdeiseed.py: tool kit
• main.py: use the dataset to run SGCL-DTI
• GCNLayer.py: a GCL layers

data sample data/heter directory

• drug.txt: list of drug names
• protein.txt: list of protein names
• disease.txt: list of disease names
• se.txt: list of side effect names
• drug_dict_map: a complete ID mapping between drug names and DrugBank ID
• protein_dict_map: a complete ID mapping between protein names and UniProt ID
• mat_drug_se.txt : Drug-SideEffect association matrix
• mat_protein_protein.txt : Protein-Protein interaction matrix
• mat_protein_drug.txt : Protein-Drug interaction matrix
• mat_drug_protein.txt: Drug_Protein interaction matrix
• mat_drug_drug.txt : Drug-Drug interaction matrix
• mat_protein_disease.txt : Protein-Disease association matrix
• mat_drug_disease.txt : Drug-Disease association matrix
• Similarity_Matrix_Drugs.txt : Drug similarity scores based on chemical structures of drugs
• Similarity_Matrix_Proteins.txt : Protein similarity scores based on primary sequences of proteins

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If you use our code, please cite our paper at the same time. Thank you!!!

Yang Li, Guanyu Qiao, Xin Gao, Guohua Wang, Supervised graph co-contrastive learning for drug–target interaction prediction, Bioinformatics, Volume 38, Issue 10, 15 May 2022, Pages 2847–2854, https://doi.org/10.1093/bioinformatics/btac164