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
pipcommand (see Installation). However, the following needs to be installed manually:
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:
| Chainer Chemistry | Chainer | RDKit | Python | | ------------------: | --------------: | -------------: | ---------------: | | v0.1.0 ~ v0.3.0 | v2.0 ~ v3.0 | 2017.09.3.0 | 2.7, 3.5, 3.6 | | v0.4.0 | v3.0 ~ v4.0 *1 | 2017.09.3.0 | 2.7, 3.5, 3.6 | | v0.5.0 | v3.0 ~ v5.0 *2 | 2017.09.3.0 | 2.7, 3.5, 3.6 | | v0.6.0 | v6.0 ~ *3 | 2017.09.3.0 | 2.7, 3.5, 3.6 | | v0.7.0 ~ v0.7.1 | v7.0 ~ | 2019.03.2.0 | 3.6, 3.7 *4 | | master branch *5 | v7.0 ~ | 2019.03.2.0 | 3.6, 3.7 |
*1: We used
FunctionNodein this PR, which is introduced after chainer v3. See this issue for details.
*2: Saliency modules only work after chainer v5.
*3: Chainer v6 is released and ChainerX is newly introduced. In order to support this new feature & API, we broke backward compatibility for chainer chemistry v0.6.0 release. See ChainerX Documentation for details.
*4: python 2.x support is dropped, following the same policy with
*5: As announced in chainer blog, further development will be limited to only serious bug-fixes and maintenance.
Chainer Chemistry can be installed using the
pipcommand, as follows:
pip install chainer-chemistry
Example to install rdkit with conda: ```bash
conda install -n base conda==4.6.14 conda install -c rdkit rdkit==2019.03.2.0 ```
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:
Please refer to the
examplesdirectory for more information.
The following graph convolutional neural networks are currently supported:
We test supporting the brand-new Graph Warp Module (GWM) -attached models for: - NFP ('nfpgwm') - GGNN ('ggnngwm') - RSGCN ('rsgcngwm') - GIN ('gingwm')
In the directory
examples/molnet_wle, we have implemented the new preprocessing ''Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks''  for several GNN architectures. Please find the Readme in that directory for the usage and the details.
The following datasets are currently supported:
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:
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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