Quantum Machine Learning for Computational Drug Discovery

Drug discovery is traditionally slow, costly, and high-risk, with timelines exceeding a decade and expenses reaching billions. This lag is particularly problematic for neglected diseases and cardiovascular conditions, which carry significant global burdens but attract limited research investment. To address this, the project proposes leveraging quantum computing and hybrid quantum-classical algorithms to accelerate drug discovery. By integrating large-scale biomedical, chemical, and epidemiological data with Quantum Enhanced Machine Learning, the initiative aims to establish a framework for data-driven therapeutic development at the host campus.
Central to the project is rigorous data acquisition and curation. Datasets will be drawn from international repositories and Canadian healthcare sources, with strong pipelines for cleaning, annotating, and ensuring ethical handling. The project will focus on exploring emerging and neglected diseases with high potential for novel drug discovery, including alveolar echinococcosis, Oropouche virus, Lyme disease, West Nile virus, as well as critical areas in cardiovascular disorders. Quantum- enhanced models trained on these curated datasets could identify subtle patterns, forecast therapeutic outcomes, and uncover opportunities for drug repurposing.

Faculty Supervisor:

Gurjit Randhawa

Student:

Partner:

Vellore Institute of Technology

Discipline:

Computer science

Sector:

Quantum Science; Biotechnology; Artificial Intelligence

University:

University of Guelph

Program: