Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry
Chainer Chemistry is a deep learning framework (based on Chainer) with applications in Biology and Chemistry. It supports various state-of-the-art models (especially GCNN - Graph Convolutional Neural Network) for chemical property prediction.
Chainer Chemistry depends on the following packages:
These are automatically added to the system when installing the library via the
pip command (see Installation). However, the following needs to be
Please refer to the RDKit documentation for more information regarding the installation steps.
Note that only the following versions of Chainer Chemistry's dependencies are currently supported:
|v0.1.0 ~ v0.3.0||v2.0 ~ v3.0||2017.09.3.0|
|v0.4.0||v3.0 ~ v4.0 *1||2017.09.3.0|
|master branch||v3.0 ~ v4.0||2017.09.3.0|
Chainer Chemistry can be installed using the
pip command, as follows:
pip install chainer-chemistry
If you would like to use the latest sources, please checkout the master branch and install with the following commands:
git clone https://github.com/pfnet-research/chainer-chemistry.git pip install -e chainer-chemistry
Sample code is provided with this repository. This includes, but is not limited to, the following:
- Training a new model on a given dataset
- Performing inference on a given dataset, using a pretrained model
- Evaluating and reporting performance metrics of different models on a given dataset
Please refer to the
examples directory for more information.
The following graph convolutional neural networks are currently supported:
- NFP: Neural Fingerprint [2, 3]
- GGNN: Gated Graph Neural Network [4, 3]
- WeaveNet [5, 3]
- SchNet 
- RSGCN: Renormalized Spectral Graph Convolutional Network 
* The name is not from the original paper - see PR #89 for the naming convention.
The following datasets are currently supported:
- QM9 [7, 8]
- Tox21 
- MoleculeNet 
- User (own) dataset
If you use Chainer Chemistry in your research, feel free to submit a pull request and add the name of your project to this list:
Other Chainer frameworks:
- Chainer: A Flexible Framework of Neural Networks for Deep Learning
- ChainerRL: Deep Reinforcement Learning Library Built on Top of Chainer
- ChainerCV: A Library for Deep Learning in Computer Vision
- ChainerMN: Scalable Distributed Deep Learning with Chainer
- ChainerUI: User Interface for Chainer
This project is released under the MIT License. Please refer to the this page for more information.
Please note that Chainer Chemistry is still in experimental development. We continuously strive to improve its functionality and performance, but at this stage we cannot guarantee the reproducibility of any results published in papers. Use the library at your own risk.
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 Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212, 2017.
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 Lars Ruddigkeit, Ruud Van Deursen, Lorenz C Blum, and Jean-Louis Reymond. Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17. Journal of chemical information and modeling, 52(11):2864–2875, 2012.
 Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data, 1:140022, 2014.
 Ruili Huang, Menghang Xia, Dac-Trung Nguyen, Tongan Zhao, Srilatha Sakamuru, Jinghua Zhao, Sampada A Shahane, Anna Rossoshek, and Anton Simeonov. Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Frontiers in Environmental Science, 3:85, 2016.
 Kipf, Thomas N. and Welling, Max. Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR), 2017.
 Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande, MoleculeNet: A Benchmark for Molecular Machine Learning, arXiv preprint, arXiv: 1703.00564, 2017.