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.

• Requirements
• Installation
  • Easy Install with Conda
  • Conda Environment
  • Docker
• FAQ and Troubleshooting
• Getting Started
• Contributing to DeepChem
  • Code Style Guidelines
  • Documentation Style Guidelines
  • Gitter
• DeepChem Publications
• Examples
• About Us
• Citing DeepChem

• pandas
• joblib
• sklearn
• numpy
• tensorflow

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.

• RDKit
• six
• MDTraj
• PDBFixer
• Pillow

pip3 install joblib pandas sklearn tensorflow pillow deepchem

conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem=2.3.0

If you want GPU support:

conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0

Note: The above commands install the latest stable version of deepchem and do not install from source. If you need to install from source make sure you follow the steps here.

Using a docker image requires an NVIDIA GPU. If you do not have a GPU please follow the directions for using a conda environment In order to get GPU support you will have to use the nvidia-docker plugin.

# This will the download the latest stable deepchem docker image into your images
docker pull deepchemio/deepchem

# This will create a container out of our latest image with GPU support
nvidia-docker run -i -t deepchemio/deepchem

# You are now in a docker container whose python has deepchem installed
# For example you can run our tox21 benchmark
cd deepchem/examples
python benchmark.py -d tox21

# Or you can start playing with it in the command line
pip install jupyter
ipython
import deepchem as dc

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.

git clone https://github.com/deepchem/deepchem.git      # Clone deepchem source code from GitHub
cd deepchem

If you don't want GPU support:

bash scripts/install_deepchem_conda.sh deepchem         # If you don't want GPU support

If you want GPU support:

gpu=1 bash scripts/install_deepchem_conda.sh deepchem         # If you want GPU support

Note : gpu=0 bash scripts/install_deepchem_conda.sh deepchem will also install CPU supported deepchem.

source activate deepchem
python setup.py install                                # Manual install
nosetests -a '!slow' -v deepchem --nologcapture        # Run tests

This creates a new conda environment deepchem and installs in it the dependencies that are needed. To access it, use the conda activate deepchem command (if your conda version >= 4.4) and use source activate deepchem command (if your conda version < 4.4).

Check this link for more information about the benefits and usage of conda environments. Warning: Segmentation faults can still happen via this installation procedure.

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

The DeepChem project maintains an extensive colelction 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.

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}
}

2.1.0