Scientists have successfully shown a quantum computer can improve the accuracy and reach of generative artificial intelligence drug discovery models. And they did it using their spare time and money leftover from other projects.
The Technical University of Denmark team ran their generative AI model for predicting proteins in conjunction with a printer-sized quantum computer built by British startup ORCA Computing, which sped up AI by linking quantum machines with traditional processors. The researchers used the hybrid technique to generate novel peptides—short chains of amino acids—capable of binding to specific proteins in the body. Doing so is a crucial step in vaccine development.
The team of researchers worked weekends and pooled unspent money from other projects because “most innovative science is too scary for foundations,” according to DTU professor Timothy Patrick Jenkins, who led the project.
Making the peptides in the laboratory and testing whether these would bind to the particular proteins showed the model produced more successful peptides than its classical counterpart, with the strongest improvements where training data was rare.
The team believe the machine could accelerate the development of personalized immunotherapies and vaccines, as well as improve drugs’ efficacy in understudied groups.
“We needed to really prove it to convince skeptics that our predictions connect to the real world,” Patrick Jenkins tells WIRED. Quantum computing remains a nascent field and faces intense scrutiny due to the technical challenges of building these machines and successfully applying them to solve problems.
Even Patrick Jenkins was initially reluctant to explore the technology: “I was a huge quantum skeptic” he says with a laugh, believing any application to his work would be “decades away.”
He and his team use big data and AI to discover proteins which could unlock new immunotherapies cheaper and faster, often funded by the Novo Nordisk Foundation. While most biological model makers are desperate for more data, a particular challenge for his team has been the lack of data on the full variety of genetic information across the human race, since most medical research has focused on Western populations. This can make it difficult to develop peptides that will work on understudied populations, such as those in Asia and Africa, he says.
His team hypothesized that embedding a quantum computer into their workflow could make it generate a more diverse set of peptides, especially for targets where they had less data, after learning that the machines had a similar effect in generating images.
The newly discovered process won’t revolutionize research yet as quantum computers are still too small to run full-scale, cutting-edge AI models, meaning better results could be achieved on a classical computer.
“Quantum is still not very powerful, so the level of complexity that we could encode wasn’t a normal-sized antibody, which is what we usually work with,” says DTU PhD student Jonathan Funk. Furthermore, finding a peptide that can bind to a specific gene is just one step in vaccine development, and wouldn’t alone yield successful drugs.
“I think it’s no surprise that lots of industrial companies think quantum is hazy and far away,” ORCA Computing chief executive officer Richard Murray tells WIRED, partly because the technology “has not ever had really clear near-term examples of usefulness.”
He says this study is novel in that it shows a near-term commercial application for quantum. His company is also applying the technology through projects with oil major BP on chemistry and carmaker Toyota on making its design process more efficient.
The DTU team will now see if it can use the workflow with more cutting-edge models and larger proteins. “We needed this as an easy way to validate that now we actually have a shot at moving the needle substantially,” says Patrick Jenkins, noting that generative AI workflows are particularly valuable in neglected diseases that receive little research money. He’s also looking at using a quantum computer to enhance his generative AI method for designing synthetic antidotes for snakebite venom.
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