My name is Jose Antonio Siguenza and I’ll be working during this summer as a contributor in DeepChem via Open Chemistry in GSoC 2022.
Currently, I’m cursing my last year studying Chemical Engineering in Ecuador. Also, I’ve been involved in research about topics related to data science, deep learning, and science at university. Over the past year, I’ve gotten to know DeepChem and I started contributing in December 2021. In general, this has been a fascinating learning path about DL models and their applications in life sciences, especially generative models. DeepChem’s community and documentation have made this journey more enjoyable.
Lately, DeepChem has decided to port some TensorFlow models to PyTorch. This project aims to successfully migrate one of the organization’s porting lists. This model will perform a mix of invertible transformations between the base and target distribution. In order to optimize the results, a Normalizing Flow object initialized with the flow layers and base distribution would be iterated computing the loss for each epoch. For this reason, transformations and flow layers will be created with their respective unittests. Finally, the implementation will include the respective documentation and a tutorial, if applicable, following DeepChem’s API.