Stable version

Caution!! : The latest stable version was published nearly a year ago. If you are a pip user or you face some errors, we recommend the nightly build version.

If you’d like to install DeepChem locally, we recommend using conda and installing RDKit with deepchem. RDKit is a soft requirement package, but many useful methods like molnet depend on it.

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

For CPU only support instead run

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

Nightly build version

You install the nightly build version via pip. The nightly version is built by the HEAD of DeepChem.

pip install tensorflow==2.3.0
pip install --pre deepchem

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

conda install -y -c conda-forge rdkit

Google Colab

The fastest way to get up and running with DeepChem is to run it on Google Colab. Check out one of the DeepChem Tutorials or this forum post for Colab quick start guides.


If you want to install using a docker, you can pull two kinds of images from DockerHub.

  • deepchemio/deepchem:x.x.x

    • Image built by using a conda package manager (x.x.x is a version of deepchem)

    • This image is built when we push x.x.x. tag

    • Dockerfile is put in docker/conda-forge directory

  • deepchemio/deepchem:latest

    • Image built by the master branch of deepchem source codes

    • This image is built every time we commit to the master branch

    • Dockerfile is put in docker/master directory

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:

# you can run our tox21 benchmark
(deepchem) root@xxxxxxxxxxxxx:~/mydir# wget
(deepchem) root@xxxxxxxxxxxxx:~/mydir# python -d tox21 -m graphconv -s random

From Source

You can install deepchem in a new conda environment using the conda commands in scripts/ 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.

First, please clone the deepchem repository from GitHub.

git clone
cd deepchem

Then, execute the shell script.

bash scripts/ cpu

If you want GPU support (we supports only CUDA 10.1):

bash scripts/ gpu

If you are using the Windows and the PowerShell:

.\scripts\install_deepchem_conda.ps1 cpu
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
pip install -e .
pytest -m "not slow" deepchem # optional