For the first time, a generative model using quantum hardware has been used by the industrial generative AI startup Zapata AI to produce cancer treatment ideas that show promise above classical models.
The University of Toronto (UofT), Insilico Medicine, and St. Jude Children’s Research Hospital were all partners with the company’s experts.
The scientists created novel KRAS inhibitors using generative AI. One of the most frequently mutated proteins in cancer, KRAS has long been considered “undruggable.”
They used technology that was simulated, quantum, and classical to run generative models. One million medication candidates were produced by each platform, and these were then screened both manually and by algorithms. An IBM 16-qubit quantum computer was utilized.
After that, 15 different compounds were created and examined using tests based on cells. Compared to current KRAS inhibitors, the two compounds produced by the quantum-enhanced generative model were distinct and had better qualities than those produced by strictly classical models.
Chief technology officer and co-founder of Zapata AI Yudong Cao remarked, “This project is an exciting demonstration of how quantum and classical computing can complement each other to deliver an end-to-end solution.”
Another excellent illustration of how the startup and academic ecosystems may benefit from one another’s capabilities to advance advancement is the partnership between Zapata, UofT, St. Jude, and Insilico. We are eager to advance this study in order to advance the identified compounds through the drug development process, apply our approach to more disease targets, and expand the applications of our quantum-enhanced generative AI to additional industrial use cases including challenging design problems.
The findings suggest that hybrid quantum generative AI, utilizing noisy intermediate-scale quantum (NISQ) equipment of today, holds great promise for drug discovery.
Alán Aspuru-Guzik, a co-founder and scientific advisor of Zapata AI as well as a professor of chemistry and computer science at the University of Toronto, said, “I have always been excited about the potential of AI and quantum computing for drug and materials discovery.”
The integration of quantum computing modules into the drug discovery pipeline is still in its early stages of development. The fact that we were able to identify a novel chemical that inhibits KRAS is fantastic. There are a lot of open-ended inquiries. It is thrilling to see that this study paves the way for future, more powerful quantum computers to demonstrate their capabilities, even if everything in this paper could also be done with a classical computer. Researchers from all across the world will be able to build on this groundbreaking experiment.
