The DeepChem project aims to democratize deep learning for science.
The DeepChem project aims to build high quality tools to democratize the use of deep learning in the sciences. The origin of DeepChem focused on applications of deep learning to chemistry, but the project has slowly evolved past its roots to broader applications of deep learning to the sciences.
The core DeepChem Repo serves as a monorepo that organizes the DeepChem suite of scientific tools. As the project matures, smaller more focused tool will be surfaced in more targeted repos. DeepChem is primarily developed in Python, but we are experimenting with adding support for other languages.
What are some of the things you can use DeepChem to do? Here's a few examples:
- Predict the solubility of small drug-like molecules
- Predict binding affinity for small molecule to protein targets
- Predict physical properties of simple materials
- Analyze protein structures and extract useful descriptors
- Count the number of cells in a microscopy image
- More coming soon...
We should clarify one thing up front though. DeepChem is a machine learning library, so it gives you the tools to solve each of the applications mentioned above yourself. DeepChem may or may not have prebaked models which can solve these problems out of the box.
Over time, we hope to grow the set of scientific applications DeepChem can address. This means we need lots of help! If you're a scientist who's interested in open source, please pitch on building DeepChem.
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'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 rdkit -c conda-forge rdkit deepchemFor CPU only support instead run
pip install tensorflow==1.14
conda install -y -c rdkit -c conda-forge rdkit deepchemThen open your python and try running.
import deepchem DeepChem is managed by a team of open source contributors. Anyone is free to join and contribute! DeepChem has weekly developer calls. You can find meeting minutes on our forums.
DeepChem developer calls are open to the public! To listen in, please email X.Y@gmail.com, where X=bharath and Y=ramsundar to introduce yourself and ask for an invite.
DeepChem is licensed under the MIT License. We actively support commercial users. Note that any novel molecules, materials, or other discoveries powered by DeepChem belong entirely to the user and not to DeepChem developers.
That said, we would very much appreciate a citation if you find our tools useful. You can cite DeepChem with the following reference.
@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}
}Support the DeepChem project by starring us on on GitHub. Join our forums at https://forum.deepchem.io to participate in discussions about research, development or any general questions. If you'd like to talk to real human beings involved in the project, say hi on our Gitter chatroom.
Important
Join our community gitter to discuss DeepChem. Sign up for our forums to talk about research, development, and general questions.
Introduction <index> Tutorial <tutorial> Installation <installation> Datasets <datasets> Data Loaders <dataloaders> Featurizers <featurizers> Splitters <splitters> Transformers <transformers> Models <models> Layers <layers> Metrics <metrics> Hyperparameter Turning <hyper> MoleculeNet <moleculenet> Metalearning <metalearning> Reinforcement Learning <rl> Applications <applications> Docking <docking> Utilities <utils>