How AI and quantum computing are accelerating cancer drug research

A research team from the University of Toronto and Insilico Medicine has successfully combined quantum computing and AI to identify potential new cancer drug molecules targeting the previously “undruggable” KRAS protein.

Research breakthrough; Scientists have identified two promising molecules that could lead to new treatments for cancers driven by KRAS mutations, which are present in 25% of human cancers and 90% of pancreatic cancers.

• The KRAS protein has historically been considered “undruggable” due to its smooth surface lacking suitable binding pockets for conventional drugs
• Researchers developed a hybrid quantum-classical model trained on 1.1 million molecules, including 650 validated KRAS inhibitors
• The team utilized Insilico Medicine’s Chemistry42 AI engine to screen and identify the most promising drug candidates

Methodology and results; The innovative approach represents the first successful use of a quantum-generative model to produce experimentally confirmed biological hits.

• The screening process identified 15 candidate molecules for laboratory testing
• Two of these molecules demonstrated strong effectiveness against multiple KRAS mutations in live cells
• The study showed that model performance improved with increased qubit count, suggesting potential scalability benefits

Expert perspective; Project director Alán Aspuru-Guzik highlighted the significance of this interdisciplinary approach.

• The research demonstrates the practical potential of combining AI and quantum computing for drug discovery
• The success marks a first-of-its-kind achievement in finding molecules that effectively interact with biological targets
• The approach could open new possibilities for developing treatments for previously challenging medical conditions

Future implications; While the study serves as a proof-of-principle, it sets the stage for expanded applications in drug discovery.

• The research team plans to apply their hybrid model to other challenging protein targets
• Further pre-clinical testing will be conducted on the two lead compounds
• As quantum computing technology advances, this approach could accelerate the development of new therapies for difficult-to-treat conditions

Looking ahead; The integration of quantum computing with AI-driven drug discovery represents a promising direction, though significant work remains to demonstrate clear advantages over classical methods and develop clinically viable treatments for KRAS-driven cancers.