This is implementation of Semi-supervised learning of hierarchical representations of molecules using neural message passing presetented at NIPS2017 Workshop on Machine Learning for Molecules and Materials.

• Chainer (<=3.1.0)
• NumPy
• SciPy
• scikit-learn
• six

You can install these packages with pip by pip install -r requirements.txt or create a conda environment with these packages installed by conda env create -n <env name> --file env.yaml.

We confirm the code with following environment.

chainer==3.1.0
numpy==1.13.3
scikit-learn==0.19.1
scipy==1.0.1
six==1.10.0

Note that this code does not work with Chainer newer than v3.1.0 due to changes made in Chainer. We will solve the problem by fixing Chainer itself. See chainer/chainer#4877 for detail.

cd unsupNFP
python train.py mutag  # Use the MUTAG dataset
python train.py ptc # Use the PTC dataset

This repository has code for the experiments of unsupervised setting only. Code for the semi-supervised setting is under preparation.

• MUTAG: BorgwardtLab/GraphKernels
• PTC: predictive-toxicology.org
• malaria: HIPS/neural-fingerprint

Nguyen, H., Maeda, S. I., & Oono, K. (2017). Semi-supervised learning of hierarchical representations of molecules using neural message passing. arXiv preprint arXiv:1711.10168 URL.

Kenta Oono (oono@preferred.jp)