Generator-Enhanced Computational Chemistry and Optimization (GECCO)

This project aims to use generative AI models to improve challenging tasks in computational chemistry and optimization. More specifically, by combining quantum computing with machine learning, the research will focus on generative AI methods to design molecules for accelerating drug discovery pipelines. Novel quantum subroutines for hybrid quantum-classical models will be designed, developed, implemented and tested extensively. Their performances will be benchmarked against classical state-of-the-art generative AI techniques to quantify the advantage introduced by the quantum component. If successful, this research could lead to faster and more effective ways to find new medicines, benefiting both the Canadian and global healthcare industry and high-tech companies, like the partner organization. This research project has the potential to revolutionize drug discovery using cutting-edge technology, and it aligns perfectly with Zapata AI’s mission: to solve the most computationally complex problems in industry with advanced computing techniques.

Faculty Supervisor:

Peng Peng

Student:

Partner:

Zapata Canada

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program: