Model creation
Models of molecules and of the biological targets they act on, built by combining quantum and classical computation.
Complex diseases have no single cause. They develop across molecular, biological, genetic and environmental levels at once. Our proprietary methodology, built on hybrid quantum–classical computation, lets us examine those levels together — complex data interrogated in depth, not filtered through prescribed assumptions. This is what our methodology was designed to do.
Integrated insight across every level of diseaseOur aim is to increase precision and reduce the time and resources spent both on discovering new therapies and on the treatment each patient receives. We do not only combine quantum and classical computation: we created our proprietary quantum-informed methodology for drug discovery.
Models of molecules and of the biological targets they act on, built by combining quantum and classical computation.
Quantum algorithms take the parts of a problem where classical approximations lose accuracy; classical computing handles the rest, in one workflow.
Candidate molecules are tested computationally before any laboratory work. Our methods extract new information from noisy data, so experiments start with the strongest candidates.
A drug discovery methodology developed in-house: our own way of selecting, testing and prioritising candidate molecules, informed by quantum chemistry.
More precise early predictions, so laboratory effort goes to the candidates most likely to succeed.
Shorter discovery cycles and lower resource use through our proprietary modelling — from the first computation to the treatment given to the patient.
Our proprietary algorithms are hardware-aware and portable. We know the limits of the physics and of the hardware, and we design every calculation to work within them.
Quantum algorithms designed to be portable across processors.
Noise, coherence and scale set what a quantum calculation can achieve. Our methods are built around those limits, not against them.
Classical high-performance computing carries the parts of each workflow that do not need a quantum computer, at scale.
Xpectra is Xtocastiq’s discovery platform. It brings target and disease evidence, compound data, quantum computation and machine learning into a single interface, so each candidate is assessed in one place — from its biological target to its toxicity profile — with our proprietary algorithms working across quantum and classical computing.
Biological targets and their links to disease, gathered in one view and connected to each candidate.
Xtocastiq’s curated compound collections, built into the platform and kept in step with our ongoing research.
A built-in environment for building and running quantum calculations alongside classical ones, without leaving the platform.
Hybrid quantum–classical toxicity assessment and machine-learning analysis, linked to the same candidate record.
Not every problem needs a quantum computer. Molecular questions, where classical approximations limit accuracy, go to quantum computation. Patient data, measured outside the clinic and at scale, goes to classical machine learning. We also run projects to build models and test molecules that may be candidates for specific patient sub-populations. Beyond our target diseases, we take on on-demand projects, using our platform to accelerate our partners’ discovery pipelines.
Molecular interactions are quantum by nature. We apply quantum computation, through our proprietary algorithms, wherever classical approximations lose precision — to design and test new therapies.
At the patient end, how biomarker information is processed matters as much as the data itself: it decides what clinicians can act on. Our trained machine-learning models are designed to turn each result into a meaningful signal.
Every result is first validated within a model we build, then validated again through in silico experiments. The same discipline applies whether the result concerns a molecule or a patient.
Further indications are in progress.
Use this form for research collaboration, partnership and general enquiries.
Last updated: 13 October 2026
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The physics behind our work, for readers who want more than the summary.
No real system is closed. Every measurement and every calculation exchanges energy and information with its environment, and noise, decoherence and finite sampling are always present. We treat this as the starting point of the physics, not as an error to remove afterwards.
Coherence, fidelity and system size bound what any quantum calculation can deliver. Our proprietary algorithms are built with explicit error budgets and are portable across processors, so each calculation is matched to what the hardware can support.
Quantum and classical computation are coupled in a single workflow. Each step runs where its physics and its cost make sense.
Every result is first validated within a model we build, then validated again through in silico experiments. The same discipline applies to molecules and to patient data.
Physicists interested in our work can reach us through the contact form.