HGDrug

The code is an official PyTorch-based implementation in the paper “A General Hypergraph Learning Framework for Drug Multi-task Predictions”

Abstract

The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for drug discovery and drug repositioning. However, chemical structure that play important role in drug properties is neglected in current biomedical networks. Here we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct a micro-to-macro drug centric heterogeneous network (DSMN), and develops a multi-branches Hyper Graph learning model, called HGDrug, for Drug multi-task predictions. The HGDrug framework is designed to capture high-order drug relationships and obtain effective drug features from DSMN network by motif-driven hypergraphs and self-supervised auxiliary task. HGDrug achieves high accuracy and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming all 6 general-purpose classical models and 8 state-of-the-art task-specific models. Experiments analysis verify the effectiveness and rationality of the model architecture and multi-branches setup, and demonstrated HGDrug can capture the approximate relationship between drugs with the same functional group. More importantly, the constructed drug-substructure interaction networks can help improve the performance of existing network models for drug-related interactions prediction tasks. The code of our model is available via https://github.com/stjin-XMU/HGDrug.

GPU environment

CUDA 10.1

create a new conda environment

conda create -n HGDrug python=3.7.3

conda activate HGDrug

download some packages

conda install -c rdkit rdkit (contruct DSMN need)

windows: pip install https://download.pytorch.org/whl/cu101/torch-1.4.0-cp37-cp37m-win_amd64.whl pip install https://download.pytorch.org/whl/cu101/torchvision-0.5.0-cp37-cp37m-win_amd64.whl

linux: pip install https://download.pytorch.org/whl/cu101/torch-1.4.0-cp37-cp37m-linux_x86_64.whl pip install https://download.pytorch.org/whl/cu101/torchvision-0.5.0-cp37-cp37m-linux_x86_64.whl

pip install -r requirements.txt

source activate HGDrug

Data Sets

./DDI_data ./DTI_data ./DDiI_data ./DSI_data

Run model

python main.py model.conf

Change prediction tasks

If the users need change the prediction task, the instructions in the model.conf need to be modified. Drug-drug interactions, the instructions need to be modified is as follows: DFI=./DDI_data/DFI.txt FFI=./DDI_data/FFI.txt Task=./DDI_data/DDiI.txt Task.name=DrugDrug

Drug-target interactions, the instructions need to be modified is as follows: DFI=./DTI_data/DFI.txt FFI=./DTI_data/FFI.txt Task=./DTI_data/DDiI.txt Task.name=DrugTarget

Drug-disease interactions, the instructions need to be modified is as follows: DFI=./DDiI_data/DFI.txt FFI=./DDiI_data/FFI.txt Task=./DDiI_data/DDiI.txt Task.name=DrugDisese

Drug-sideeffect interactions, the instructions need to be modified is as follows: DFI=./DSI_data/DFI.txt FFI=./DSI_data/FFI.txt Task=./DSI_data/DDiI.txt Task.name=DrugSideeffect

Output

Output as the results of "AUROC" and "AUPR".