AI Startup Secures $11M to Supercharge Cancer Drug Discovery

ProteinQure lands $11M to accelerate AI-designed peptide drugs, targeting cancer, brain, and kidney treatments with speed and precision.

6 Jun 2025

ProteinQure, a Toronto-based biotechnology company focused on AI-designed peptide therapeutics, has raised $11mn to advance its lead drug candidate into clinical trials and expand into new disease areas.

The financing round, led by Heron Rock Fund with participation from Golden Ventures and Kensington Capital, marks a transition from early research to clinical development. The company’s lead compound, developed for triple-negative breast cancer, is expected to enter trials in the third quarter of 2025.

“This is a defining moment for ProteinQure and for the field,” said Lucas Siow, chief executive and co-founder. “We’re advancing what we believe to be the first AI-designed peptide therapeutic into the clinic.”

Peptide-based drugs, which are smaller and more targeted than traditional biologics, are seen as offering high specificity and reduced side effects. ProteinQure’s platform uses computational tools to design these molecules, aiming to shorten drug development timelines and improve success rates.

Beyond oncology, the company plans to pursue treatments for neurological and kidney diseases, areas where current drug options remain limited.

Investors have shown increasing interest in companies using artificial intelligence in drug discovery, viewing them as potential challengers to large pharmaceutical groups struggling with rising costs and extended development cycles.

However, AI-generated therapies remain largely untested in clinical settings. Regulators are still assessing how best to evaluate drug candidates designed by algorithms, and few such therapies have reached late-stage trials.

Despite these uncertainties, backers argue that AI is poised to play a central role in the future of pharmaceutical innovation.

If successful, ProteinQure’s approach could accelerate not only its own cancer pipeline but also validate a broader model in which software-led drug design reshapes how new treatments are brought to market.