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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)
- 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
* sixYou 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])