DeepChem

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:

• 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

Requirements

• pandas
• joblib
• sklearn
• numpy
• tensorflow

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.

• rdkit
• six
• mdtraj

Super easy install via pip3

pip3 install joblib pandas sklearn tensorflow pillow deepchem

Easy Install via Conda

conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem=2.1.0

Note: Easy Install installs the latest stable version of deepchem and does not install from source. If you need to install from source make sure you follow the steps here.

Using a Docker Image

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

Installing from source in a conda environment

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.

FAQ and Troubleshooting

1. deepchem currently supports both Python 2.7 and Python 3.5, and is supported on 64 bit Linux and Mac OSX. Note that DeepChem is not currently maintained for Python 3.6 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

Two good tutorials to get started are Graph Convolutional Networks and Multitask_Networks_on_MUV. Follow along with the tutorials to see how to predict properties on molecules using neural networks.

Afterwards you can go through other tutorials, and look through our examples in the examples directory. 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.

Benchmarks

In depth benchrmarking tables for DeepChem models are available on MoleculeNet.ai

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 possible due to notable contributions from many people including Peter Eastman, Evan Feinberg, Joe Gomes, Karl Leswing, Vijay Pande, Aneesh Pappu, Bharath Ramsundar and Michael Wu (alphabetical ordering). DeepChem was originally created by Bharath Ramsundar with encouragement and guidance from Vijay Pande.

DeepChem started as a Pande group project at Stanford, and is now developed by many academic and industrial collaborators. DeepChem actively encourages new academic and industrial groups to 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:

@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}
}

Version

2.1.0