Kuano is aiming to transform the discovery of enzyme inhibitor drugs using a platform that combines quantum transition state simulation and AI led chemistry.

Enzyme catalysis occurs via thermodynamically favoured quantum transition states, which represent an attractive conformation for targeting enzyme inhibitors. Kuano’s proprietary in silico approach rapidly simulates the quantum transition state and generates a quantum pharmacophore. The quantum pharmacophore is used with AI/ML approaches to identify transition state inhibitors in novel chemical space.

According to Kuano, enzyme transition state-based drug discovery has previously been time consuming and costly. As a result, structure-based drug discovery for enzyme inhibitors has focused on an imperfect model of ligand-enzyme interactions. Kuano says its approach rapidly and cost-effectively generates inherently better enzyme inhibitors that provide strong binding, high selectivity and are more robust to mutations.

In addition to in-house discovery programs (in epigenetics, protein degradation, immunometabolism, infectious disease and others) the company is also pursuing drug discovery collaborations with pharmaceutical and biotech companies seeking new approaches to novel, first in class, best in class or next generation inhibitors for validated or intractable enzyme targets.