we provide an example script to run experiments with the features already extracted from our dataset:

• Run run_deepDTnet.m: predict drug-target interactions, and evaluate the results with cross-validation. Note: See the "Tutorial" section below for a detailed instruction on how to run deepDTnet on your own dataset.

• auc.m: evaluation script
• compute_similarity.m: compute Jaccard similarity based on association network(i.e., drug-disease, drug-side-effect, and protein-disease networks)
• imf_train.mexa64: pre-built binary file of PU-Matrix Completion algorithm
• PUMC.m: predict drug-target interactions (DTIs) with PU-Matrix Completion
• run_deepDTnet.m: example code of running deepDTnet for drug-target prediction

• drug_dict.txt: list of drug unique identifier and drug names
• protein_dict.txt: list of protein unique identifier and protein names
• disease_dict.txt: list of disease unique identifier and disease names
• se_dict.txt: list of side effect unique identifier and side effect names
• drugdrug.txt: Drug-Drug interaction matrix
• drugDisease.txt: Drug-Disease association matrix
• drugsideEffect.txt: Drug-SideEffect association matrix
• drugsim1network.txt: Drug chemical similarity matrix
• drugsim2network.txt: Drug therapeutic similarity matrix
• drugsim3network.txt: Drug sequence similarity matrix
• drugsim4network.txt: Drug biological processes similarity matrix
• drugsim5network.txt: Drug cellular component similarity matrix
• drugsim6network.txt: Drug molecular function similarity matrix
• proteinprotein.txt: Protein-Protein interaction matrix
• proteinDisease.txt: Protein-Disease association matrix
• proteinsim1network.txt: Protein sequence similarity matrix
• proteinsim2network.txt: Protein biological processes similarity matrix
• proteinsim3network.txt: Protein cellular component similarity matrix
• proteinsim4network.txt: Protein molecular function similarity matrix

We provided the pre-trained vector representations for the 15 networks, which were used to produce the results in our paper.

This directory contains code necessary to run the DNGR algorithm.

• Run main.m: an example script to generate a low-dimensional vector representation of features for each node in the Drug chemical similarity network. Note: For different networks, you need to fine tune the parameters in BuildNets.m. The original version of this code base was originally from http://shelson.top/, and we owe many thanks to Shaosheng Cao for making his code available.

• Supplementary_Table_3.xlsx: an excel table including 4,978 experimentally validated drug-target interactions connecting 732 approved drugs and 1,915 human targets.
• Supplementary_Table_4.xlsx: an excel table including all target lists for G-protein-coupled receptors (GPCRs), kinases, nuclear receptors (NRs), and ion channels (ICs).
• Supplementary_Table_5.xlsx: an excel table including novel drug-target interactions predicted by deepDTnet with score above the threshold.

We test the code on Ubuntu 16.04 with Matlab R2016a installed

1. Put interaction/association/similarity matrices in the data/ folder.
2. Create a simNet/ folder under deepDTnet/ and run compute_similarity.m in the src/ folder, which will compute the Jaccard similarity of drugs and proteins, based on the three association matrices mentioned above.
3. run main.m in the DNGR/core folder for 2 interaction networks ,10 similarity networks in the data/ folder and 3 similarity networks in the simNet/ folder, which will learn the feature vectors of drugs and proteins for the 15 networks and save them in the feature/ folder.
4. Specify parameters and set the path of feature vectors in run_deepDTnet.m and execute it. This script will predict the drug-target interactions and evaluate the results using a five-fold cross-validation.