QureGenAI Shows Its 15-Qubit Quantum AI Drug-Design Center in Xi'an to 15 Shaanxi Drug Makers on 28 October 2026: What the Company Claims and What Has Been Published
How QureGenAI presented quantum AI drug design to 15 Shaanxi drug makers in Xi'an
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
On the afternoon of 28 October 2026, representatives of fifteen pharmaceutical companies from Shaanxi province toured a quantum computing center in Xi'an that is built for drug research. The host, QureGenAI (医图生科 in Chinese), used the visit to present its case for quantum AI drug design to companies that make peptides, traditional Chinese medicines and generic drugs.
The Xi'an Advanced Manufacturing Association organized the visit, and the Chang'an Xiandao Life Sciences Industry Innovation Center co-hosted it. According to the event report published on ifeng.com on 5 November 2026, the companies attending included the Shaanxi Pharmaceutical Research Institute, Huikang Biotech, Jinhua Pharmaceutical and Lijun Pharmaceutical. Officials from the Xi'an industry and information technology bureau also attended.
The center itself is a joint project of the Chang'an Xiandao innovation center and Xi'an Xiandao Yitu Quantum Technology, a company linked to QureGenAI. The report describes it as housing the first 15-qubit superconducting quantum computer in northwest China, coupled to classical computers in a hybrid cluster for biomedical work. The company also calls its offering the world's first end-to-end quantum computing solution aimed at industrial drug design and discovery. That claim comes from the company and the report repeats it without any comparison to other platforms.
Li Xiaoran, chief executive of QureGenAI and chief scientist of the center, listed four areas where the company applies its methods: design of new drugs, protein structure prediction, optimization of fermentation processes, and activity screening of natural products. He cited two examples from cancer research, a prodrug design for pancreatic cancer and drug development against the KRAS target, a protein that is mutated in many pancreatic, colorectal and lung tumors.
Li argued that quantum computing has an advantage that classical methods cannot match in two specific steps of chemistry: calculations on strongly correlated electronic systems, and searches for the transition states of chemical reactions. Both steps are hard for standard computational chemistry, so the choice of target is sensible. The report gives no data for either example, however. It names no molecule, no accuracy figure and no comparison with a classical calculation on the same problem.
The same applies to the largest number in the report. Li said the team had applied combined quantum and AI methods to traditional medicine design, enzyme engineering and aqueous two-phase extraction, and that peptide batch production had reached an efficiency of several dozen times that of traditional processes. No study, patent or customer is cited for that figure, so a reader cannot yet check how it was measured or how much of it came from the quantum part of the workflow.
QClairvoyance Splits Eight Small Ligand-Like Molecules Into Fragments on IBM Quantum Computers: What Its Journal of Chemical Physics Study of 19 October 2026 Shows
What QClairvoyance ran on IBM quantum processors, and what its Journal of Chemical Physics study shows
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
QClairvoyance Quantum Labs, a start-up in Secunderabad, India, has published a study in which eight small molecules of the kind medicinal chemists use as building blocks were simulated on IBM superconducting quantum processors. The paper appeared online in The Journal of Chemical Physics on 19 October 2026 and combines sample-based quantum diagonalization with density matrix embedding, so that each molecule is cut into pieces small enough for current hardware.
Every molecule's computed ground-state energy landed within chemical accuracy, the conventional 1 kcal/mol error benchmark in computational chemistry. The yardstick was an exact classical calculation of the same fragmented problem, which makes the result checkable. The molecules weigh between 40 and 76 daltons, while most oral drugs weigh several hundred, and the calculations used STO-3G, the smallest standard basis set.
The Economic Times, which interviewed founder Col. Sai Shankar P, a former technical manager to India's National Quantum Mission, described the findings as an initial proof of concept and not yet a breakthrough in drug discovery. That reading matches the paper itself.
The peer-reviewed article, in volume 165 of the journal as article 154108, lists Ashish Kumar Patra, Anurag K. S. V., Sai Shankar P., Ruchika Bhat, Raghavendra V., Rahul Maitra and Jaiganesh G., the corresponding author. Five work at QClairvoyance, with Bhat also at the University of Arizona. Raghavendra V. is at the SRM Institute of Science and Technology in Chennai and Maitra at the Indian Institute of Technology Bombay; the paper declares that both are paid consultants to the company. A preprint on arXiv first appeared on 27 November 2025 and was last revised on 17 July 2026.
The eight molecules are cyanic acid, formaldehyde oxime, O-methylhydroxylamine, methyl isocyanate, acetaldehyde oxime, urea, nitrosyl chloride and hydroxythiocyanate. The authors chose them because they lack the tidy symmetry of molecules used in earlier tests and mix different kinds of chemical bonds. They note that urea derivatives and compounds related to methoxyamine already have a place in medicinal chemistry. None of the eight is a drug candidate, and the paper makes no such claim.
The main runs took place on 4 and 5 June 2025 on ibm_sherbrooke, a 127-qubit IBM Eagle processor. A second experiment on hydroxythiocyanate used ibm_boston, an IBM Heron processor. The work ran on IBM Quantum Credits from the IBM Quantum Startups Program, and the authors report no specific grant funding.
Daiichi Sankyo Uses QSimulate's QUELO Quantum Chemistry Software in Several Drug Discovery Programs to Predict Binding Affinity: What the June 2026 AWS Account Shows
How Daiichi Sankyo uses QSimulate's quantum chemistry to predict drug binding
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
Daiichi Sankyo is using software from the Boston start-up QSimulate to bring quantum chemistry into one of the most routine and most expensive judgments in drug discovery: how tightly a candidate molecule will bind to its protein target. The main public account is a Japanese-language blog on the Amazon Web Services site, published on 11 June 2026.
That blog was written jointly by scientists from Daiichi Sankyo's Smart Research Institute II, by QSimulate, and by an AWS solutions architect, and it describes how the company evaluated and then adopted QSimulate's QM-FEP method, a version of free energy perturbation that uses quantum mechanics for the energy of the interaction between drug and protein.
Predicting binding affinity well matters because it decides which molecules a medicinal chemistry team actually makes. Each round of the design, make, test and analyze cycle costs weeks of synthesis and assay work. A computer model that ranks candidates more reliably lets chemists spend that time on the compounds most likely to work, and lets them drop weak ideas before anyone orders reagents.
No quantum computer is involved here. QSimulate's software runs quantum mechanical calculations on ordinary graphics processors in the cloud. The interest for medicine lies in a large pharmaceutical company putting physics-level chemistry into its everyday design work today, years before quantum hardware is expected to take on the same problem.
The Daiichi Sankyo authors explain the problem in practical terms. Their teams already use structure-based drug design and conventional free energy perturbation, which estimates binding by simulating the gradual transformation of one molecule into another inside the protein. That method depends on a classical force field, a fixed set of rules for how atoms push and pull on each other. The authors write that more accurate energy functions are needed, especially for the chemical modalities they expect to grow more complex, where conventional FEP has struggled with non-classical interactions and with covalent drugs that form a chemical bond with their target.
Saveetha University's DFT Study of "DTPB" in Results in Chemistry Describes Penoxsulam, a Rice Herbicide: What It Means for Computational Drug Discovery
A Saveetha University DFT study describes the rice herbicide penoxsulam
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
A computational chemistry paper from Saveetha University in Chennai presents a fluorinated sulfonamide called DTPB as a molecule with nonlinear optical promise and a possible starting point for drug design. The full chemical name in its title identifies the same compound as the registered rice herbicide penoxsulam, and the paper's abstract does not use that name.
The study appeared in Results in Chemistry, volume 31, as article 103949, and was covered on 11 October 2026 by the science news site Bioengineer.org under the headline that quantum chemistry "reveals hidden power" in the molecule. For a reader who follows computational drug discovery, the paper is a useful case in how to read a calculated portrait of a compound: what the calculations can establish, what they cannot, and what a quick lookup in a public chemical database adds before anyone talks about medicine.
The authors are L. Sasikala, P. V. Avinash, M. Suganthi, A. Suvitha, Sheeba Britto, S. Vimal and Marimuthu Prabhaharan, four of them listed at Saveetha University and one at St. Joseph's Institute of Technology. The article is open access under the DOI 10.1016/j.rechem.2026.103949.
The team used density functional theory, the standard quantum-mechanical method for computing a molecule's electron structure on ordinary computers, at the B3LYP/6-311G(d,p) level. According to the abstract, the optimized geometry is well defined, and the gap between the highest occupied and lowest unoccupied molecular orbitals falls between 4.61 and 4.89 electronvolts across the solvent models used. The authors read that as moderate stability with favorable internal charge transfer.
The headline number concerns optics. The calculated first-order hyperpolarizability is 1.5 × 10-30 electrostatic units, which the abstract puts at about eleven times the value for urea, a common reference material. Maps of electrostatic potential and electron localization point to the oxygen- and nitrogen-containing regions as the reactive sites. A graph-theory section reports topological indices of 170, 201 and 878.
NYU's Tautomer-Predictor Skips Quantum Calculations and Flags 126 PDBbind Ligands: What the September 2026 Chemical Science Paper by Pan, Han, Han and Zhang Shows
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
A single hydrogen atom can sit in two places on the same drug molecule. When it moves, the pattern of single and double bonds shifts with it, and the molecule presents a different face to the protein it is meant to bind. Chemists call these interchangeable forms tautomers, and choosing the wrong one at the start of a computer-aided drug design project can distort the docking scores and free-energy estimates that follow.
A paper in the Royal Society of Chemistry journal Chemical Science, "Deep learning of tautomer stability from crystallographic proton positions" by Xiaolin Pan, Chao Han, Fengyang Han and Yingkai Zhang of New York University, published online in September 2026, attacks this problem with an unusual source of training data. The team mined hydrogen positions resolved in small-molecule crystal structures from the Cambridge Structural Database, built more than 1.1 million tautomeric states from them, and trained a graph neural network to predict tautomer stability directly from a molecule's two-dimensional structure. The authors state that the model needs no 3D conformers and no quantum-mechanical calculations, which is what makes it fast enough for structure-based drug discovery at library scale.
The quantum-medicine interest lies in what the network replaces. Quantum chemistry has long been the accurate but slow reference for deciding which tautomer a molecule prefers, and it is also one of the problems quantum computing companies cite when they pitch molecular simulation to pharmaceutical buyers. Here a classical model trained on experimental crystal data takes over a large part of that job on a single GPU node.
The results come from retrospective tests on public structure collections. The study screened no new compounds in the laboratory and involved no patients, and its training labels describe molecules in the crystal, which is a different environment from a protein pocket or blood plasma.
QC Ware and IonQ Mapped Four Orbitals of a Cytochrome P450nor Active Site Onto Eight Qubits of IonQ Forte on 1 September 2026
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
How tightly a candidate binds its target is one important factor in whether a drug works, and metal centers are among the difficult cases to calculate: an iron atom sitting in the middle of an enzyme. On 1 September 2026 QC Ware and IonQ said they had measured a four-orbital slice of one such site on a quantum computer and landed close to the classical answer. QC Ware's Promethium platform did the classical preparation, IonQ's Forte trapped-ion processor took the quantum measurement through Amazon Braket, and the target was the heme active site of cytochrome P450nor. The workflow calculated an electrostatic interaction energy within 0.5 kcal/mol of classical benchmarks, roughly four percent, inside the one kcal/mol threshold conventionally called chemical accuracy.
The IonQ half of a workflow already run on IBM hardware
This is a follow-up. On 7 August 2026 this Monitor read the same QC Ware Promethium workflow running a nitric oxide reductase calculation on IBM's 156-qubit Heron processor. The platform, the enzyme family and the hybrid division of labor are unchanged; the processor underneath is what is new, which makes hardware portability the company's actual claim. Portability is not comparability: the August disclosure carried no numerical result that today's figure could be set against. Today's release adds four disclosures the August account did not carry: the 115-atom active-site model, its more than 1,000 molecular orbitals, the reduction to a four-orbital active space mapped onto eight qubits, and the 0.5 kcal/mol comparison itself.
What the quantum machine actually did
The quantum step ran on eight qubits of a 36-qubit machine, measuring a four-orbital slice carved out of a model containing more than a thousand molecular orbitals. Promethium built the model, identified the strongly correlated region, reduced it automatically to that active space, and computed the final interaction energies classically after Forte measured the qubits in a single basis. Everything before and after the quantum slice happened on classical hardware.
Which enzyme, and why the framing needs care
P450nor sits in the cytochrome P450 superfamily whose monooxygenase members carry out most human drug metabolism, and the release says exactly that. P450nor itself is something else: a fungal CYP55 nitric oxide reductase, characterized in Fusarium oxysporum among other fungi, reducing nitric oxide to nitrous oxide and taking its electrons straight from reduced nicotinamide adenine dinucleotide, NADH. The release names no organism and no accession. It is a well-characterized computational test case, which is a good reason to choose it for a hardware demonstration and a poor reason to describe the run as modeling human drug metabolism.
What the result can and cannot establish
The comparison target was a classical benchmark, so the classical answer existed first, and the companies report agreement for this known case. The announcement names no preprint, reports no shot count, gives no uncertainty on the 0.5 kcal/mol figure and does not identify which classical method produced the benchmark.
Inside the 156-Qubit Enzyme Calculation
QC Ware reports a molecular calculation on quantum hardware
QC Ware says it calculated the electrostatic interaction energy of nitric oxide reductase by combining GPU-accelerated molecular modeling, classical chemistry methods, and quantum measurements on IBM's 156-qubit Heron processor. The enzyme is chemically demanding because its active region contains metal. The public announcement makes the electrostatic interaction energy calculation concrete, but it does not disclose the molecular partition, circuit design, measurement count, error mitigation, reference value, or final numerical error. The release therefore supports a precise statement: QC Ware reports that a medically relevant class of molecular calculation reached named quantum hardware. It does not yet show that the quantum step improved the result.
The architecture has a clear division of labor
The hybrid chemistry workflow combines Promethium, GPU-accelerated molecular modeling, classical chemistry methods, and quantum measurements. The release does not disclose how work was partitioned among them. IBM's published description of Heron and System Two provides useful architectural context: its quantum processors operate with classical runtime servers and methods that divide larger calculations. The QC Ware release is the source for the later 156-qubit hardware claim. It also says the demonstration is not currently an integrated Promethium product capability. That sentence prevents a hardware claim from being mistaken for a production service.
The missing benchmark defines the next milestone
The announcement reports no quantum advantage and offers no comparison against a strong classical workflow for the same chemical task. Qubit count cannot supply that missing result. A buyer would need comparative accuracy, resources, runtime, repeatability, and a decision consequence for chemists. The public record therefore places the work at TRL 3: a vendor-reported hardware proof of concept for one molecular property. Its product boundary is commercially informative because it separates an experimental module from the platform available today. The next persuasive record would show what the quantum measurements add at a fixed cost or error, and whether that contribution changes a research decision.