Physics-Based Intelligence for Drug Discovery

Quantum Lens, our quantum and AI platform, provides the depth needed to guide high-stakes discovery decisions and enable the discovery of drug candidates.

We bring therapeutic design into focus through the lens of fundamental physics.

The Quantum Lens is a sophisticated intelligence system directed by Kuano’s scientific experts.

We don't just provide access to technology; we provide the expertise to deploy the right workflows to give insights into your molecular system.

The Expertise

Our team identifies the physical hurdles stalling your program - deciding which factors need to be modelled and at what level of accuracy.

The Technology

Our platform provides the depth needed to illuminate molecular behaviours that standard, high-throughput tools cannot resolve.

Our platform enables faster hits, better selectivity in fewer wet lab cycles.

The Insight

Customers receive actionable discovery decisions, not raw data. We navigate the complexity of the platform so your team can focus on the resulting therapeutic breakthroughs.

Where Generalist Platforms Stop, We Start

Standard Workflow with 5 steps with additional specialised workflows are used for Covalent and Transition State Drug Design

Target and 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.

Quantum and AI Depth

We model transition states, enzyme dynamics and other phenomena that affect drug discovery at quantum resolution. Most platforms approximate this away. That approximation costs you missed opportunities.

Expert-Led Delivery

You don’t get a login. You get drug hunters and computational scientists who work alongside your team, interpret results, and translate findings into actionable chemistry.

Challenging Targets

Where typical computational methods plateau, we act as edge-case specialists for targets requiring sophisticated insights;

Structured engagements. Clear deliverables. Real results

Every engagement is built around a defined problem, a clear scope, and a measurable outcome

Starter

Focused on one specific problem to understand

  • Target data analysis

  • Ligand data analysis

  • Simulation evaluation

  • Initial Compounds

Timeline: 4 - 8 weeks

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Core*

Understanding and rounds of chemistry design

  • All in Starter

  • Extensive simulation

  • Comprehensive reporting

  • Optimised Compounds

Timeline: 3 - 6 months

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Premium

Understanding, design, and consultancy and partnership

  • All in Core

  • Extensive Drug Hunting support

  • Extensive quantum consulting

  • Fully integrated project

Timeline: 6 -12 months

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Custom Research

We have many tools for many problems

  • Understand how your ligand binds

  • Explore how “quantum makes a difference”

  • Assess existing and proposed ligands

Timeline: Custom

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Case Studies

Results, not just research

The client needed understanding where there allosteric ligand binds. Traditional methods would have taken months. Using Kuano’s Quantum Lens, we delivered evidence of binding location in weeks

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The client needed assessment of their why their covalent ligands were not binding. Using Kuano’s Quantum Lens, we delivered understanding what quantum features are required to optimise their compounds.

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Covalent Platform

  • Suitable for multiple target classes: kinases, GPCRs, ion channels, enzymes, etc

  • Kinetic-Driven Potency: Modeling binding as a dynamic reaction to identify compounds with superior residence time and potency.

  • Structural Bias Elimination: Automatically identifying link-orbitals via DMET to only include the orbitals that contribute to bonding process - more efficient calculations.

  • Site-Specific Selectivity: Tuning warhead reactivity via Fukui Indices to prevent off-target bonding and reduce systemic toxicity.

  • Validated Covalent Engagement: Distinguishing true chemical bonds from non-specific proximity using Mayer Bond Orders to gain a quantitative measure of covalency.

  • Predictive Inactivation Rates: Leveraging Two-Body Correlators for understand how the covalent bond forms. 

  • Electronic Affinity Mapping: Capturing sub-atomic "entanglement" to find potency drivers that standard docking and FEP consistently overlook.

The Transition State Design Platform

  • Transition State Informed Lead Selectivity: Creating unique "quantum fingerprints" of the transition state to achieve world-class selectivity across highly homologous isoforms and mutants. 

  • Generative Drug-Like Innovation: Translating complex transition-state insights into practical, low-molecular-weight leads that medicinal chemists actually want to synthesise.

  • Electronic Context Mapping: Factoring in sub-atomic entanglement and local environments to predict binding affinity where standard forcefields fail.

  • High-Velocity TS-Design: Streamlining transition-state workflows to deliver high-resolution, physics-first results in months, significantly accelerating drug-like transition state inhibitor design. 

The Quantum Lens

  • Suitable for multiple target classes and modalities: kinases, GPCRs, ion channels, enzymes, glues, peptides and PPI’s

  • FMO-Driven Affinity Mapping: Deconstructing binding scores into residue-specific electronic contributions to identify precise, actionable potency drivers.

  • Entanglement-Based Target Mapping: Measuring complex electronic environments to identify non-obvious binding interactions for high-affinity optimisation.

  • Intelligence-Driven Molecular Generation: Translating deep quantum insights into novel, synthesisable chemical structures optimised for superior pharmacological performance in novel IP space

  • Expanding Multi-Scale Physics Architecture: Deploying and expanding a specialised suite of advanced technologies to resolve confounding variables in the most challenging targets.