NeoDTI

NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions (preprint).

Requirements

• Tensorflow (tested on version 1.0.1 and version 1.2.0)
• tflearn
• numpy (tested on version 1.13.3 and version 1.14.0)
• sklearn (tested on version 0.18.1 and version 0.19.0)

Quick start

To reproduce our results:

1. Unzip data.zip in ./data.
2. Run NeoDTI_cv.py to reproduce the cross validation results of NeoDTI. Options are:
   -d: The embedding dimension d, default: 1024.
-n: Global norm to be clipped, default: 1.
-k: The dimension of project matrices, default: 512.
-r: Positive and negative. Two choices: ten and all, the former one sets the positive:negative = 1:10, the latter one considers all unknown DTIs as negative examples. Default: ten.
-t: Test scenario. The DTI matrix to be tested. Choices are: o, mat_drug_protein.txt will be tested; homo, mat_drug_protein_homo_protein_drug.txt will be tested; drug, mat_drug_protein_drug.txt will be tested; disease, mat_drug_protein_disease.txt will be tested; sideeffect, mat_drug_protein_sideeffect.txt will be tested; unique, mat_drug_protein_drug_unique.txt will be tested. Default: o.
3. Run NeoDTI_cv_with_aff.py to reproduce the cross validation results of NeoDTI with additional compound-protein binding affinity data. Options are:
   -d: The embedding dimension d, default: 1024.
-n: Global norm to be clipped, default: 1.
-k: The dimension of project matrices, default: 512.

Data description

• 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_drug_drug.txt : Drug-Drug interaction matrix.
• mat_protein_disease.txt : Protein-Disease association matrix.
• mat_drug_disease.txt : Drug-Disease association matrix.
• mat_protein_drug.txt : Protein-Drug interaction matrix.
• mat_drug_protein.txt : Drug_Protein interaction matrix.
• Similarity_Matrix_Drugs.txt : Drug & compound similarity scores based on chemical structures of drugs ([0,708) are drugs, the rest are compounds).
• Similarity_Matrix_Proteins.txt : Protein similarity scores based on primary sequences of proteins.
• mat_drug_protein_homo_protein_drug.txt: Drug_Protein interaction matrix, in which DTIs with similar drugs (i.e., drug chemical structure similarities > 0.6) or similar proteins (i.e., protein sequence similarities > 40%) were removed (see the paper).
• mat_drug_protein_drug.txt: Drug_Protein interaction matrix, in which DTIs with drugs sharing similar drug interactions (i.e., Jaccard similarities > 0.6) were removed (see the paper).
• mat_drug_protein_sideeffect.txt: Drug_Protein interaction matrix, in which DTIs with drugs sharing similar side effects (i.e., Jaccard similarities > 0.6) were removed (see the paper).
• mat_drug_protein_disease.txt: Drug_Protein interaction matrix, in which DTIs with drugs or proteins sharing similar diseases (i.e., Jaccard similarities > 0.6) were removed (see the paper).
• mat_drug_protein_unique: Drug_Protein interaction matrix, in which known unique and non-unique DTIs were labelled as 3 and 1, respectively, the corresponding unknown ones were labelled as 2 and 0 (see the paper for the definition of unique).
• mat_compound_protein_bindingaffinity.txt: Compound-Protein binding affinity matrix (measured by negative logarithm of Ki).

All entities (i.e., drugs, compounds, proteins, diseases and side-effects) are organized in the same order across all files.

Contacts

If you have any questions or comments, please feel free to email Fangping Wan (wfp15[at]mails[dot]tsinghua[dot]edu[dot]cn) and/or Jianyang Zeng (zengjy321[at]tsinghua[dot]edu[dot]cn).