DeepChem is a python library that provides a high quality open-source toolchain for deep-learning in drug discovery, materials science, quantum chemistry, and biology.
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!
DeepChem is licensed under the MIT License. We actively support commercial users. Note that any novel molecular entities found through DeepChem belong entirely to the user and not to DeepChem developers.
New users should check out installation instructions.
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. 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.