Case Studies
Short reports showcasing quantum-informed design and AI-driven drug discovery in action
Challenge: Off target effects limit efficacy
DNMT1 is a cancer-linked enzyme sharing substrates with DNMT3a and DNMT3b, making selective drug design difficult.
Current treatments bypass the active site by binding to DNA, which, while effective, often results in side effects.
Engineering Selectivity in a Challenging Target
Quantum pharmacophore guides rapid creation of novel, selective hits for DNMT1
Solution: Transition State inhibitors
Even nearly identical enzymes accelerate reactions differently. Kuano exploits these subtle distinctions, targeting the reaction's Transition State to design highly specific binders.
In just 2 months, we progressed from crystal structures to candidate inhibitors.
Result: Selective DNMT1 inhibitors
Synthesizing and testing only 40 compounds, we identified 3 novel chemotypes active against DNMT1 with high selectivity over DNMT3a and DNMT3b.
Two chemotypes match the potency of established series, representing a major step toward safer, targeted therapies.
Let us help you find your next advanced hit compound
Challenge: Efficient hit design for a novel target
NOTUM is a carboxylesterase that inactivates Wnt ligands, which are dysregulated in colorectal, liver, and pancreatic cancers. Inhibiting NOTUM can restore tumor suppression.
With few existing inhibitors, a novel NOTUM-targeting therapy would represent a first-in-class clinical breakthrough.
Rapid Generation of High Quality Hits
From crystal structure to novel inhibitors in 50 compounds
Solution: Transition State state drug design
Transition state-based design identifies inhibitors with ideal charge and shape properties for target engagement.
The Quantum Lens platform identified the Transition State using advanced simulation and defined a physics based pharmacophore for de novo drug design. Our generative AI then created bespoke NOTUM inhibitors.
Result: Production of potent and selective inhibitors
Kuano produced nanomolar-potent NOTUM inhibitors with proven selectivity across family-specific, pathway, and broad off-target panels. Demonstrating how Quantum Lens powered Transition State drug design can design potency and selectivity into novel compounds.
Kuano not only found advanced hits for NOTUM but made significant strides towards a first-in-class candidate for the treatment of bowel cancer.
Challenge: Unexplained selectivity between closely related phosphatases
In 2024, researchers found natural product inhibitors selective across the PTP subfamily. While sequence differences usually drive selectivity, SHP2 shows tenfold higher binding affinity than SHP1, despite identical active sites. Deciphering this mechanism is vital for creating drugs that minimize harmful off-target effects.
Pinpointing Selectivity With Quantum Energy Profiling
Quantum interaction analysis decodes selectivity beyond sequence differences in SHP1/2 phosphatases
Solution: Quantum Interaction Analysis
To decode the selectivity differences between these phosphatases, we integrated classical and quantum methodologies. Using our Quantum Interaction approach, we generated quantitative insights that clarified the drivers of selectivity.
The analysis uses Fragment Molecular Orbital (FMO) and pair interaction energy decomposition analysis (PIEDA) to:
Evaluate attractive and repulsive forces for a comprehensive understanding of complexation
Account for charge transfer in ligand binding
Identify peripheral residues beyond the catalytic site that facilitate selectivity
Result: Peripheral residue energies drive selectivity
Quantum Lens analysis revealed that residues outside the active site accounted for more than 50% of inhibitor binding contributions.
Binding disparities between SHP1 and SHP2 can be explained by unfavorable interactions distant from the active site within the SHP1 complex (with lysine residue). Recognizing these unintuitive interactions aids in the optimization of compounds with marginal selectivity.
Challenge: Understanding covalent systems to enhance warhead selectivity
Covalent inhibitors provide therapeutic advantages through stable target bonding. However, indiscriminate "warhead" binding to off-targets causes side effects.
Chemical nuances modulate reactivity within binding pocket microenvironments. Standard glutathione (GSH) experiments frequently fail to capture essential features for achieving warhead selectivity.
Traditional design often fails to predict these microenvironment effects. For instance, the transition between aliphatic and aryl acrylamide warheads in developing Ritlecitinib (a JAK3 inhibitor) relied on empirical observation rather than predictive models.
Solution: Application of Kuano’s Quantum Fingerprints
Kuano evaluates inhibitor reactivity and enzyme microenvironment influences using advanced metrics.
Our Quantum Lens applies quantum information techniques (including one-orbital entropy and mutual information) to reveal subtle electronic and energetic molecular traits typically missed by traditional analysis.
Result: Warhead Selectivity Disparities Explained
While aliphatic and aryl warheads show similar GSH interaction profiles in isolation, accounting for the binding microenvironment reveals a different reality. Despite previous research suggesting aryl superiority, mutual information plots align with experimental data to show that aliphatic systems achieve better bond satisfaction.
Key findings include:
Superior aliphatic bond satisfaction confirmed by mutual information plots.
Significant impacts from interaction reorganization around a remote ligand oxygen atom.
These results link quantum interactions to inhibitor efficacy, demonstrating the power of computational modeling to validate experiments and predict the performance of novel AI-generated designs.
Quantum Informed Rational Covalent Inhibitor Design
Explaining the subtle determinants of target specific reactivity
Posters
We believe that the future of drug discovery lies at the intersection of advanced computation and deep scientific insight. Our team is committed to presenting our latest advancements at leading scientific conferences, driving forward the fields of AI in chemistry and simulation and drug target analysis. We share our expertise with the community and showcase our incredible scientists.
Explore the posters we have presented, which showcase how our proprietary technology is leveraging simulation, quantum chemistry, and artificial intelligence to accelerate the identification and optimization of novel therapeutic compounds.
