AugLiChem
Welcome to AugLiChem! The augmentation library for chemical systems. This package supports augmentation for both crystaline and molecular systems, as well as provides automatic downloading for our benchmark datasets, and easy to use model implementations. In depth documentation about how to use AugLiChem, make use of transformations, and train models is given on our website.
Installation
AugLiChem is a python3.8+ package.
Linux
It is recommended to use an environment manager such as conda to install AugLiChem. Instructions can be found here. If using conda, creating a new environment is ideal and can be done simply by running the following command:
conda create -n auglichem python=3.8
Then activating the new environment with
conda activate auglichem
AugLiChem is built primarily with pytorch and that should be installed independently according to your system specifications. After activating your conda environment, pytorch can be installed easily and instructions are found here.
torch_geometric needs to be installed with conda install pyg -c pyg -c conda-forge.
Once you have pytorch and torch_geometric installed, installing AugLiChem can be done using PyPI:
pip install auglichem
MacOS ARM64 Architecture
A more involved install is required to run on the new M1 chips since some of the packages do not have official support yet. We are working on a more elegant solution given the current limitations.
First, download this repo.
If you do not have it yet,, conda for ARM64 architecture needs to be installed. This can be done with Miniforge (which contains conda installer) which is installed by following the guide here
Once you have miniforge compatible with ARM64 architecture, a new environment with rdkit can be i nstalled. If you do not specify python=3.8 it will default to python=3.9.6 as of the time of writing th is.
conda create -n auglichem python=3.8 rdkit
Now activate the environment:
conda activate auglichem
From here, individual packages can be installed:
conda install -c pytorch pytorch
conda install -c fastchan torchvision
conda install scipy
conda install cython
conda install scikit-learn
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cpu.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+cpu.html
pip install torch-geometric
Before installing the package, you must go into setup.py in the main directory and comment out rdkit-pypi and tensorboard from the install_requires list since they are already installed. Not commenting these packages out will result in an error during installation.
Finally, run:
pip install .
Usage guides are provided in the examples/ directory and provide useful guides for using both the molecular and crystal sides of the package. Make sure to install jupyter before working with examples, using conda install jupyter. After installing the package as described above, the example notebooks can be downloaded separately and run locally.
Authors
Rishikesh Magar*, Yuyang Wang*, Cooper Lorsung*, Hariharan Ramasubramanian, Chen Liang, Peiyuan Li, Amir Barati Farimani
*Equal contribution
Paper
Our paper can be found here
Citation
If you use AugLiChem in your work, please cite:
@misc{magar2021auglichem,
title={AugLiChem: Data Augmentation Library ofChemical Structures for Machine Learning},
author={Rishikesh Magar and Yuyang Wang and Cooper Lorsung and Chen Liang and Hariharan Ramasubramanian and Peiyuan Li and Amir Barati Farimani},
year={2021},
eprint={2111.15112},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
License
AugLiChem is MIT licensed, as found in the LICENSE file. Please note that some of the dependencies AugLiChem uses may be licensed under different terms.
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