For the First Time, Quantum-Enhanced Generative AI Generates Viable Cancer Drug Candidates

Zapata Computing, Inc. (“Zapata AI” or the “Company”), the Industrial Generative AI company, today announced that its scientists, in collaboration with Insilico Medicine, the University of Toronto, and St. Jude Children’s Research Hospital have demonstrated the first instance of a generative model running on quantum hardware outperforming state-of-the-art classical models in generating viable cancer drug candidates. The research points to a promising future of hybrid quantum generative AI for drug discovery using today’s quantum devices.

In the study, the researchers utilized generative AI to develop novel KRAS inhibitors, a critical focus in cancer therapy historically deemed “undruggable” due to its intrinsic biochemical properties. Generative models running on classical hardware, quantum hardware (specifically, a 16-qubit IBM device), and simulated quantum hardware generated one million drug candidates each, which were then filtered algorithmically and by humans. The resulting 15 molecules were then synthesized and tested through cell-based assays. The two molecules generated by the quantum-enhanced generative model were distinct from existing KRAS inhibitors and showed a superior binding affinity over the molecules generated by purely classical models.

“This project is an exciting demonstration of how quantum and classical computing can complement each other to deliver an end-to-end solution,” said Yudong Cao, CTO and co-founder at Zapata AI. “The collaboration between Zapata, UofT, St. Jude and Insilico is also a great example of how the startup and university ecosystems can leverage each other's advantages to drive progress. We’re looking forward to taking this research further to move the discovered molecules through the drug discovery pipeline, apply our methodology to other disease targets, and extend our quantum-enhanced generative AI to other industrial use cases with complex design challenges.”

The research is currently published on ArXiv as it awaits peer review. The study is a follow-up to a study published by the team in 2023, in collaboration with Foxconn, that first showed the promise of quantum generative AI for drug discovery.

“This research provides further validation of the potential of Insilico’s generative AI engine, Chemistry42, to be combined with quantum-augmented generative models in order to develop novel therapeutic possibilities for difficult-to-drug targets in cancer and other indications,” says Alex Zhavoronkov, PhD, founder and co-CEO of Insilico Medicine. “This represents an important early step toward a more advanced drug discovery future and we look forward to working with Zapata AI and Alán Aspuru-Guzik at the University of Toronto to further develop these methods."