Applying state-of-art NLP models to molecular representation

Molecular generative methods are at the heart of our computational platform. We use cutting edge deep neural networks in order to generate unseen molecules based on existing conditions or molecular space. Since external projects typically require different architectures, we are continuously expanding our generative methods toolbox with new approaches. This project aims to augment our internal toolbox with large pretrained NLP models for molecular representation and generation. During this project, the intern will have to train and benchmark existing architecture with large datasets but also contribute/integrate new models such as the transformers provided by Hugging Face into our existing API.

Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Valence Discovery Inc

Discipline:

Computer science

Sector:

Pharmaceuticals; Artificial Intelligence; Health and Related Sciences & Technology

University:

Université de Montréal

Program: