Quantum Lens

Making the undruggable druggable

A physics native platform built to see what others can’t: revealing novel chemical space and generating diverse hits for the hardest targets in drug discovery.

Patented quantum and AI technology, built for biotech, pharma and CRO partners who need speed and precison at enterprise scale.

Drug Discovery
Enterprise

Drug Discovery Service

Covalent Inhibitor Design

Quantum Lens for Covalent Inhibitor Design is a great way to evaluate our service and platform capabilities since the work is proven to be well-defined, with a clear scope and timeline and demonstrates the platform’s precision on a real target.

Using quantum fingerprinting, we characterise warhead reactivity and selectivity in your target's true protein environment, going beyond conventional HOMO/LUMO approximations that routinely fail for reactive systems.

What Quantum Lens Platform delivers:

✓ Targeted covalent inhibitor design
✓ Warhead selection and reactivity prediction
✓ Phosphatase selectivity solutions
✓ Transition-state drug design
✓ Unlocking undruggable enzyme targets
✓ Scaffold hopping and lead optimisation
✓ Quantum fingerprinting for novel chemistry

As a Service:

✓ End to end project management/onboarding
✓ High level tech and physics specialists

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Quantum Lens

Enterprise Scale

Quantum Lens can enhance your capabilities inhouse as a biotech and your commercial offer as a CRO.

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Scoping Discussion

  • Target & Data Analysis

    Understanding what data is known about the target / off-targets and existing ligands and what are the specific goals of the project.

  • Quantum Simulation

    Deploying our toolsets and workflows to probe and analyse the protein-ligand system, not just classical approximations.

  • Feature Extraction

    Converting the raw quantum data into actionable design rules or models, identifying the specific potency drivers, selectivity windows or criteria to achieve the specific goals of the project.

  • AI Driven Design

    Give the high-fidelity electronic features into reaction-based generative models to navigate chemical space with surgical accuracy.

  • Optimised Lead Candidates

    Delivering high-potency, synthesisable structures with rationale for further optimisation.