A study co-led by University of Toronto (U of T) researchers and Insilico Medicine has demonstrated the potential of quantum computing and artificial intelligence (AI) to transform the drug discovery pipeline. 

“It’s an exciting time to be working at the interface of chemistry, quantum computing and AI. This first-of-its-kind study shows that AI, with the help of quantum computers, can successfully find molecules that interact with biological targets,” said Alán Aspuru-Guzik, who directed the project and is a Professor of Chemistry and Computer Science at U of T.  

In the study published in Nature Biotechnology, the researchers combined quantum computing and generative AI with classical computing methods to create molecules targeting the cancer-driving protein KRAS, which had previously been considered “undruggable.”  

Mutations in KRAS drive uncontrolled cell growth and are present in about one in four human cancers.  

Despite their prevalence and impact, there are currently only two FDA-approved drugs that specifically target mutant KRAS. However, clinical data show that these drugs extend life by only a few months compared to traditional chemotherapy. 

Proof of principle study

The team optimised their models by first training them with a custom-built dataset of 1.1 million molecules, including 650 that were previously experimentally validated to block KRAS and 250,000 obtained via the open-source ultra-large virtual screening platform VirtualFlow. 

Next, the research team used Insilico Medicine’s generative AI engine Chemistry42 to screen the molecules and identify the 15 most promising candidates for lab testing. Of the 15, two molecules stood out for their strong ability to target multiple different versions of mutated KRAS in live cells, highlighting their potential as anti-cancer drugs. 

“With computational approaches like this, we have the potential to shorten the preclinical phase of drug discovery by years,” said Igor Stagljar, a co-investigator on the study and Professor of Biochemistry and Molecular Genetics at the Donnelly Centre at U of T’s Temerty Faculty of Medicine. 

“This is a proof-of-principle study but does not provide any sign of significant quantum advantage,” added Aspuru-Guzik, who is also director of U of T’s Acceleration Consortium and a member of the Vector Institute. “This paper shows that quantum computers can be incorporated into modern accelerated AI-driven drug discovery pipelines. And as quantum computers grow in power, our algorithms will hopefully perform better and better.”  

The researchers are now applying their hybrid quantum-classical model to other undruggable protein targets.  

They are also using their model to optimise the design of the two top candidates against KRAS, with the goal of moving these compounds to further preclinical testing in animal models.

Diana Spencer, Senior Digital Content Editor, DDW