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)

• Host: GitHub
• URL: https://github.com/pfnet-research/hierarchical-molecular-learning
• Owner: pfnet-research
• License: mit
• Created: 2018-06-20T10:56:20.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-06-20T11:01:26.000Z (over 6 years ago)
• Last Synced: 2024-10-30T15:50:41.647Z (19 days ago)
• Language: Python
• Size: 53.7 KB
• Stars: 14
• Watchers: 12
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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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])