multitask-gnn's Introduction

Multitask Learning On Graph Neural Networks

In this repository we provide the code that is necessary to reproduce the results from the paper Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions.

Dependencies

The list of libraries that you need to install to execute the code:

torchvision=0.3.0
networkx=2.3
rdflib=4.2.2
torch_spline_conv=1.1.0
plyfile=0.7
numpy=1.16.4
six=1.12.0
scipy=1.3.0
torch_sparse=0.4.0
googledrivedownloader=0.4
torch_cluster=1.4.3
torch=1.1.0
matplotlib=3.1.0
h5py=2.9.0
pandas=0.24.2
torch_scatter=1.3.1
Pillow=6.1.0
gdist=1.0.3
scikit_learn=0.21.3

You can install all of those libraries through :

pip install -r requirements.txt

We have also included pytorch geometric in the present repository. If you would like to install it instead, you need to have PyTorch 1.2.0 and install it through

pip install torch-geometric

The libraries torch-scatter, torch-sparse and torch-cluster need to be installed first. Please, have a look at the installation guide for further information.

rdkit

rdkit cannot be installed through pip. Please, refer to their installation page to install it on your system.

Usage

To test the algorithms, you need to change the parameters in config.cfg and run:

python3 main.py

This will run a k-fold cross-validation with a train/test split like explained in the paper. The only possibilities for the parameter "model" are "GIN", "GGRNet" and "GAIN", which corresponds to the models of the paper.

Citation

@article{mtlgnn2019,
    author  = {Fabio Capela and Vincent Nouchi and Ruud Van Deursen and Igor V. Tetko and Guillaume Godin},
    title   = {Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions},
    journal = {arXiv:1910.13124},
    year    = {2019}
}