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.
Quick Start¶
The fastest way to get up and running with DeepChem is to run it on Google Colab. Check out one of the DeepChem Tutorials.
If you’d like to install DeepChem locally,
pip install deepchem
Then open your IDE or text editor of choice and try running the following code with python.
import deepchem
About Us¶
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.
Important
API Reference
- Data
- MoleculeNet
- Featurizers
- Splitters
- Transformers
- Model Classes
- Scikit-Learn Models
- Gradient Boosting Models
- Deep Learning Infrastructure
- Keras Models
- PyTorch Models
- PyTorch Lightning Models
- Jax Models
- Hugging Face Models
- Layers
- Metrics
- Hyperparameter Tuning
- Metalearning
- Reinforcement Learning
- Docking
- Utilities