DeepChem.jl development projects – Summer of Code

Towards DeepChem.jl: Combining Machine Learning with Chemical Knowledge

We have been developing the AtomicGraphNets.jl package, which began modestly as a Julia port of CGCNN, but now has plans to expand to a variety of more advanced graph-based methods for state-of-the-art ML performance making predictions on atomic systems. In support of this package, we are also developing ChemistryFeaturization.jl, which contains functions for building and featurizing atomic graphs from a variety of standard input files. ChemistryFeaturization will eventually form the bedrock of a DeepChem.jl umbrella organization to host a Julia-based port of the popular Deepchem Python package.

Some of the features we're excited about working on include:

Recommended Skills: Basic graph theory and linear algebra, some knowledge of chemistry

Expected Results: Contributions of new features in the eventual DeepChem.jl ecosystem

Mentors: Rachel Kurchin