Website | Documentation | Colab Tutorial | Discussion Forum | Gitter
DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, quantum chemistry, and biology.
DeepChem currently supports Python 3.7 through 3.9 and requires these packages on any condition.
DeepChem has a number of "soft" requirements.
If you face some errors like ImportError: This class requires XXXX, you may need to install some packages.
Please check the document about soft requirements.
DeepChem stable version can be installed using pip or conda as
pip install deepchemor
conda install -c conda-forge deepchem
Deepchem provides support for tensorflow, pytorch, jax and each require a individual pip Installation.
For using models with tensorflow dependencies, you install using
pip install deepchem[tensorflow]For using models with torch dependencies, you install using
pip install deepchem[torch]For using models with jax dependencies, you install using
pip install deepchem[jax]If GPU support is required, then make sure CUDA is installed and then install the desired deep learning framework using the links below before installing deepchem
- tensorflow - just cuda installed
- pytorch - https://pytorch.org/get-started/locally/#start-locally
- jax - https://github.com/google/jax#pip-installation-gpu-cuda
In zsh square brackets are used for globbing/pattern matching. This means you
need to escape the square brackets in the above installation. You can do so
by including the dependencies in quotes like pip install --pre 'deepchem[jax]'
The nightly version is built by the HEAD of DeepChem. It can be installed using
pip install --pre deepchemIf you want to install deepchem using a docker, you can pull two kinds of images.
DockerHub : https://hub.docker.com/repository/docker/deepchemio/deepchem
deepchemio/deepchem:x.x.x- Image built by using a conda (x.x.x is a version of deepchem)
- The x.x.x image is built when we push x.x.x. tag
- Dockerfile is put in
docker/tagdirectory
deepchemio/deepchem:latest- Image built from source codes
- The latest image is built every time we commit to the master branch
- Dockerfile is put in
docker/nightlydirectory
You pull the image like this.
docker pull deepchemio/deepchem:2.4.0If you want to know docker usages with deepchem in more detail, please check the document.
If you try install all soft dependencies at once or contribute to deepchem, we recommend you should install deepchem from source.
Please check this introduction.
The DeepChem project maintains an extensive collection of tutorials. All tutorials are designed to be run on Google colab (or locally if you prefer). Tutorials are arranged in a suggested learning sequence which will take you from beginner to proficient at molecular machine learning and computational biology more broadly.
After working through the tutorials, you can also go through other examples. To apply deepchem to a new problem, try starting from one of the existing examples or tutorials and modifying it step by step to work with your new use-case. If you have questions or comments you can raise them on our gitter.
- Weights & Biases: Track your DeepChem model's training and evaluation metrics.
Join us on gitter at https://gitter.im/deepchem/Lobby. Probably the easiest place to ask simple questions or float requests for new features.
DeepChem is managed by a team of open source contributors. Anyone is free to join and contribute!
If you have used DeepChem in the course of your research, we ask that you cite the "Deep Learning for the Life Sciences" book by the DeepChem core team.
To cite this book, please use this bibtex entry:
@book{Ramsundar-et-al-2019,
title={Deep Learning for the Life Sciences},
author={Bharath Ramsundar and Peter Eastman and Patrick Walters and Vijay Pande and Karl Leswing and Zhenqin Wu},
publisher={O'Reilly Media},
note={\url{https://www.amazon.com/Deep-Learning-Life-Sciences-Microscopy/dp/1492039837}},
year={2019}
}