Researchers at the Technical University of Denmark have demonstrated that a printer-sized quantum computer can improve the accuracy and scope of generative artificial intelligence models for drug discovery. They achieved this result using spare time and leftover funds from other projects.
The setup
The team ran their protein-predicting AI alongside a quantum processor built by British startup ORCA Computing. This hybrid approach links quantum machines with traditional processors to speed up the work. The researchers used the technique to generate new peptides, which are short chains of amino acids capable of binding to specific proteins in the body. This step is essential for vaccine development.
Working over weekends and pooling unspent money, the group tested whether the lab-made peptides would bind to the intended proteins. The model produced more successful peptides than its classical counterpart. The strongest improvements appeared where training data was scarce.
The team believes the machine could accelerate the development of personalised immunotherapies and vaccines. It may also improve drug efficacy in understudied groups.
“I was a huge quantum skeptic”
“We needed to really prove it to convince skeptics that our predictions connect to the real world,” Patrick Jenkins tells WIRED. Jenkins, a professor at DTU who led the project, notes that quantum computing remains a nascent field facing intense scrutiny due to the technical challenges of building these machines and successfully applying them to solve problems.
Even 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.
Limitations and commercial hope
The newly discovered process will not revolutionise 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 would not 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 advanced 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 is also looking at using a quantum computer to enhance his generative AI method for designing synthetic antidotes for snakebite venom.
What it means
For researchers working on neglected diseases, this workflow offers a practical way to validate new methods without waiting for fully mature quantum technology. It provides a tangible path forward for generating diverse peptide candidates in areas where data is thin, potentially leading to better treatments for populations that have historically been overlooked in medical trials.
