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https://github.com/pfnet-research/hierarchical-molecular-learning

Implementation of "Semi-supervised learning of hierarchical representations of molecules using neural message passing" (arXiv:1711.10168)
https://github.com/pfnet-research/hierarchical-molecular-learning

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Implementation of "Semi-supervised learning of hierarchical representations of molecules using neural message passing" (arXiv:1711.10168)

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README

        

This is implementation of [Semi-supervised learning of hierarchical representations of molecules using neural message passing](https://arxiv.org/abs/1711.10168) presetented at NIPS2017 [Workshop on Machine Learning for Molecules and Materials](http://www.quantum-machine.org/workshops/nips2017/).

# Dependency

* 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 --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](https://github.com/chainer/chainer/issues/4877) for detail.

# Usage

```python
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.

# Data source

* MUTAG: [BorgwardtLab/GraphKernels](https://github.com/BorgwardtLab/GraphKernels)
* PTC: [predictive-toxicology.org](https://www.predictive-toxicology.org)
* malaria: [HIPS/neural-fingerprint](https://github.com/HIPS/neural-fingerprint)

# Reference

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](https://arxiv.org/abs/1711.10168).

# Contact

Kenta Oono ([email protected])