Quantum Lens
Physics-Based Intelligence for Drug Discovery
Quantum Lens, our quantum and AI platform, provides the depth needed to guide high-stakes discovery decisions and support the discovery and design of differentiated drug candidates.
We bring therapeutic design into focus through the lens of fundamental physics.
The Expertise
We identify 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.
The Insight
Customers receive actionable discovery decisions, not raw data. We navigate the complexity of the science, so your team can focus on decisions that move programmes forward.
Selected Areas of Expertise
1. Covalent Reactivity
Predict reactivity and understand covalency in ways other methods miss, enabling accurate tuning and design
Suitable for multiple target classes: kinases, GPCRs, ion channels, enzymes.
Site-Specific Selectivity: Reveal how the interplay between the ligand and the target amino acid environment dictates inhibitor selectivity
Electronic Structure Analysis: Uncover distal relationships between warhead reactivity and scaffold properties that are often overlooked by conventional FEP and docking techniques
Dynamic Reaction Modelling: Elucidate intricate reaction mechanism details to optimize potency and residence time
Predict Inactivation Rates: Robust Binding and bonding metrics distinguish the true drivers of potency and support generative design, compound prioritization, and SAR understanding
Efficient System Description: Streamline study turnaround times and minimize costs by identifying the precise level of quantum detail required
Generative Modelling: Facilitate de novo, scaffold-based, and warhead-based discovery by translating platform insights into novel ligands
The Quantum Lens provides a transformative approach to designing targeted covalent inhibitors by combining advanced simulation and analysis techniques (such as MM-ML, DMET, Two-Body Correlators, and Entanglement measures) with innovative AI and fingerprinting methodologies
2. Hard-to-Drug Enzyme Targets
Design potent, selective inhibitors for hard-to-drug enzymes by exploiting non-obvious binding interactions and Transition State modelling.
Enzyme Inhibitors inspired by enzyme function for exquisite selectivity and potency
AI-Driven Hit Generation: Employ Quantum Fingerprints—physics-based templates—to design compounds that medicinal chemists actually want to synthesise using minimal data
Unrestricted Chemical Exploration: Access novel chemical space through Quantum Fingerprints to locate the optimal compound for any specific target
Novel Design Insights: Utilize automated simulations of enzyme chemistry and dynamics to reveal and exploit hidden features in drug design
Precision Selectivity Engineering: Translate complex transition-state insights into superior selectivity, even across highly similar mutants and isoforms
Lead Optimisation: Combine data and physical insights, down to sub-atomic entanglement, to refine and improve compounds binding and ADMET properties
Optimise Study Efficiency: Streamline turnaround times and minimize costs by identifying the precise level of dynamic and quantum detail required
Quantum Lens technology facilitates the integration of physics-based insights from automated pipelines with medicinal chemistry and wetlab data to resolve critical bottlenecks in inhibitor design
3. Allosteric / Shallow Pockets
Discover insights, resolve confounding non-intuitive variables to make confident design decisions.
Suitable for multiple target classes and modalities: kinases, GPCRs, ion channels, enzymes, glues, peptides and PPI’s
Allosteric Effect Simulation: Leveraging automated, high-speed dynamic simulations to decode allosteric pathways and evaluate how molecular variations influence target behavior.
Quantum-Level Site Analysis: Assessing intricate electronic landscapes, such as those in metalloenzymes, to uncover hidden interactions essential for high-affinity lead optimization.
Quantum Interaction Profiling: Breaking down binding affinity into specific electronic contributions at the residue level to pinpoint exact, actionable drivers of potency.
De Novo Structure Synthesis: Converting Quantum Lens data into original, chemically accessible structures tailored for peak pharmacological efficacy within proprietary IP domains.
Through our Quantum Lens, we utilize a growing array of sophisticated tools to simulate multi-scale physics, resolving the complex variables inherent in the most demanding therapeutic targets