Shionogi Wins NEDO's ¥40 Million Quantum Computing Prize for Drug Design: What the Scientific Reports Study With D-Wave's Annealer Shows
What Shionogi won in Japan's NEDO quantum computing contest, and what its annealing study shows
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
Shionogi, the Osaka drug maker, took first place and a ¥40 million prize in the Quality of Life category of the NEDO Challenge on quantum computing, Japan's state-funded contest for quantum solutions to social problems. D-Wave Quantum announced the win on 28 October 2026 and tied it to a peer-reviewed study in which its quantum annealing hardware helped a generative AI model design drug-like molecules.
The study is the part a medicinal chemist can check. Published open access in Scientific Reports on 9 July 2026, Table 1 of the published article reports that 96.97 percent of the molecules generated with the annealer's help were chemically valid, against 61.95 percent for the same model with a classical sampler. The quantum-assisted model also produced a larger share of compounds with high drug-likeness scores than its training set contained, while repeating itself more often.
Those are computed scores on a public chemistry dataset. The study reports no synthesis and no biological testing of any generated molecule, and the prize money is meant to fund the next steps of that research.
NEDO, the New Energy and Industrial Technology Development Organization that operates under Japan's Ministry of Economy, Trade and Industry, held the award ceremony in Tokyo on 15 October 2026 and posted the official list of winners the following day. The contest ran in three areas: Society 5.0, Quality of Life and Cool Japan. First prize in each area carries ¥40 million, second ¥20 million and third ¥10 million. First place in Society 5.0 and Cool Japan was left vacant, which makes Shionogi the only first-place winner this season.
NEDO's list describes Shionogi's entry in Japanese as solving drug discovery problems through two complementary uses of quantum computers: generating the structures of candidate compounds, and evaluating the interaction energy between molecules. The other Quality of Life prizes went to two teams working on the gut and wider microbiome: Mori-san Daisuki Club in second place with a quantum search method for designing gut bacterial communities, and Q4Health in third place for microbiome design aimed at next-generation antimicrobial treatments.
The D-Wave study maps onto the first of Shionogi's two tracks. Quantum Computing Report wrote on 23 October 2026 that all nine general prize winners used IBM Quantum hardware, and it described Shionogi's entry as a quantum and high-performance computing hybrid that evaluates binding energy and generates structures through a method called Deep QSCI. D-Wave's announcement says the award covered Shionogi's broader research, including, among other achievements, the annealing study. A reader should therefore treat the prize as recognition for a two-part program run on more than one kind of quantum machine.
Simulated Quantum Annealing Ranked Known Drug Pairs First for Diabetes and Rheumatoid Arthritis: Ramos, Coutinho and Magano in Quantum Machine Intelligence, 23 June 2026
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
Three researchers from Porto and the German Aerospace Center have written drug-pair selection as an energy problem that a quantum annealer could solve, and tested it on diabetes, rheumatoid arthritis, asthma and brain tumors. In simulation, the lowest-energy answers were dominated by drug combinations that doctors already use, and one of the top new suggestions for rheumatoid arthritis matches a pairing that a 799-patient trial found to roughly double remission rates.
The paper, by Diogo Ramos, Bruno Coutinho and Duarte Magano, appeared open access in Quantum Machine Intelligence on 23 June 2026 as article 70 of volume 8. It builds on the Complementary Exposure idea from network medicine, turns it into an optimization problem, and samples answers with simulated quantum annealing, a classical computer program that imitates how quantum annealing hardware behaves. Its central test is how often validated drug combinations appear among the top-ranked answers when they make up less than a tenth of a percent of the candidates.
Combination therapy is standard across chronic disease and oncology, because two drugs together can allow lower doses and fewer side effects. The search space is the problem. With more than a thousand approved drugs and thousands of diseases, the number of possible pairs grows far faster than any laboratory or trial program can test them.
For a pharmacology team or a hospital formulary committee, the study offers a clear example of what quantum optimization can and cannot yet contribute to that search. The ranking method works in simulation on four diseases. No quantum computer was used, and the authors state plainly that their predictions are hypotheses for experts to examine.