# DeepChem

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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.

Table of contents:


DeepChem requires these packages on any condition.

Soft Requirements

DeepChem has a number of "soft" requirements. These are packages which are needed for various submodules of DeepChem but not for the package as a whole.


Install via conda (Recommendation)

RDKit is a soft requirement package, but many useful methods like molnet depend on it. If you use conda, we recommend installing RDKit with deepchem.


Coming soon...


pip install tensorflow==1.14
conda install -c rdkit -c conda-forge rdkit deepchem==2.3.0

If you want GPU support:

pip install tensorflow-gpu==1.14
conda install -c rdkit -c conda-forge rdkit deepchem==2.3.0

Install via pip (WIP)

You are able to try to install deepchem via pip using the following command.
However, pip installation is under development, so this command may not work well.


Coming soon...


pip install pandas pillow scikit-learn==0.22 tensorflow==1.14 deepchem==2.2.1.dev54

If you want GPU support:

pip install pandas pillow scikit-learn==0.22 tensorflow-gpu==1.14 deepchem==2.2.1.dev54

Install from source

You can install deepchem in a new conda environment using the conda commands in scripts/install_deepchem_conda.sh. Installing via this script will ensure that you are installing from the source. The following script requires conda>=4.4 because it uses the conda activate command. (Please see the detail from here)

First, please clone the deepchem repository from GitHub.

git clone https://github.com/deepchem/deepchem.git
cd deepchem

Then, execute the shell script.

bash scripts/install_deepchem_conda.sh deepchem

If you are using the Windows and the PowerShell:

.\scripts\install_deepchem_conda.ps1 deepchem

Before activating deepchem environment, make sure conda has been initialized.
Check if there is a (base) in your command line. If not, use conda init <YOUR_SHELL_NAME> to activate it, then:

conda activate deepchem
python setup.py install
pytest -m "not slow" deepchem # optional

Check this link for more information about the installation of conda environments.

Install using a Docker

If you want to install using a docker, you can pull two kinds of images.
DockerHub : https://hub.docker.com/repository/docker/deepchemio/deepchem

First, you pull the image you want to use.

docker pull deepchemio/deepchem:2.3.0

Then, you create a container based on the image.

docker run --rm -it deepchemio/deepchem:2.3.0

If you want GPU support:

# If nvidia-docker is installed
nvidia-docker run --rm -it deepchemio/deepchem:2.3.0
docker run --runtime nvidia --rm -it deepchemio/deepchem:2.3.0

# If nvidia-container-toolkit is installed
docker run --gpus all --rm -it deepchemio/deepchem:2.3.0

You are now in a docker container which deepchem was installed. You can start playing with it in the command line.

(deepchem) root@xxxxxxxxxxxxx:~/mydir# python
Python 3.6.10 |Anaconda, Inc.| (default, May  8 2020, 02:54:21)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import deepchem as dc

If you want to check the tox21 benchmark:

(deepchem) root@xxxxxxxxxxxxx:~/mydir# wget https://raw.githubusercontent.com/deepchem/deepchem/master/examples/benchmark.py
(deepchem) root@xxxxxxxxxxxxx:~/mydir# python benchmark.py -d tox21 -m graphconv -s random

FAQ and Troubleshooting

  1. DeepChem currently supports Python 3.5 through 3.7, and is supported on 64 bit Linux and Mac OSX. Note that DeepChem is not currently maintained for older versions of Python or with other operating systems.

  2. Question: I'm seeing some failures in my test suite having to do with MKL Intel MKL FATAL ERROR: Cannot load libmkl_avx.so or libmkl_def.so.

    Answer: This is a general issue with the newest version of scikit-learn enabling MKL by default. This doesn't play well with many linux systems. See BVLC/caffe#3884 for discussions. The following seems to fix the issue

    conda install nomkl numpy scipy scikit-learn numexpr
    conda remove mkl mkl-service
  3. Note that when using Ubuntu 16.04 server or similar environments, you may need to ensure libxrender is provided via e.g.:

sudo apt-get install -y libxrender-dev

Getting Started

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.


Join us on gitter at https://gitter.im/deepchem/Lobby. Probably the easiest place to ask simple questions or float requests for new features.

About Us

DeepChem is managed by a team of open source contributors. Anyone is free to join and contribute!

Citing DeepChem

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:

    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},