The DeepChem Project
The DeepChem project aims to democratize deep learning for science.
What is DeepChem?
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.
If you'd like to install DeepChem locally, we recommend installing deepchem which is nightly version and RDKit. RDKit is a soft requirement package, but many useful methods depend on it.
pip install tensorflow~=2.4 pip install --pre deepchem conda install -y -c conda-forge rdkit
Then open your python and try running.
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.
.. toctree:: :glob: :maxdepth: 1 :caption: Get Started get_started/installation get_started/requirements get_started/tutorials get_started/examples get_started/issues get_started/Docker-tutorial
.. toctree:: :glob: :maxdepth: 1 :caption: API Reference api_reference/data api_reference/moleculenet api_reference/featurizers api_reference/splitters api_reference/transformers api_reference/models api_reference/layers api_reference/metrics api_reference/hyper api_reference/metalearning api_reference/rl api_reference/docking api_reference/utils
.. toctree:: :glob: :maxdepth: 1 :caption: Development Guide development_guide/licence development_guide/scientists development_guide/coding development_guide/ci development_guide/infra