QC Ware and IonQ report achieving 0.5 kcal/mol accuracy in calculating electrostatic interaction energy, a result within the 1 kcal/mol threshold considered chemically accurate for enzyme modeling. The companies demonstrated this precision using a hybrid quantum-classical workflow on IonQ’s Forte hardware, modeling the heme active site of cytochrome P450nor, an enzyme critical to human drug metabolism, the company says. This approach delivered more than double the accuracy of standard classical methods, potentially improving drug candidate ranking and early detection of metabolic risks.
QC Ware and IonQ Forte Achieve 4% Accuracy in P450nor Enzyme Modeling
The companies paired GPU-accelerated classical pre-processing within QC Ware’s Promethium platform with quantum measurements performed on IonQ’s Forte trapped-ion quantum computer via Amazon Braket to calculate electrostatic interaction energy. This result was 0.5 kcal/mol. The modeling focused on cytochrome P450nor, a specific nitric oxide reductase, indicating a move beyond theoretical quantum computing toward practical application in a complex biological system. Promethium first built and preprocessed a 115-atom model of the enzyme’s active site, containing over 1,000 molecular orbitals, then isolated a critical region for quantum measurement.
IonQ Forte’s all-to-all qubit connectivity proved crucial, allowing complex entangling gates to execute without the routing overhead common in systems with limited connectivity, according to the company. Dr. Kin-Joe Sham, Co-Founder and COO at QC Ware, said that running the same hybrid workflow on IonQ’s trapped-ion architecture, following their recent demonstration on other quantum hardware, shows that Promethium’s approach to combining classical and quantum computing is not tied to a single type of quantum hardware.
Accurately predicting how tightly a drug candidate binds to its target, particularly at complex metal centers like the iron site in P450nor, is vital for ranking candidates and identifying potential metabolic risks. Scott Millard, Chief Business Officer at IonQ, explained that every month spent advancing a drug candidate on flawed metabolic data is wasted time and increases risk.
QC Ware and IonQ have shown that hybrid quantum-classical workflows can predict certain binding behavior accurately enough for discovery teams to confidently rank candidates and catch toxicity risks early, and they believe that’s a real quantum impact on real health outcomes, the firm reports. The demonstration, supported by Amazon Web Services cloud compute credits, highlights the potential for seamless integration between classical GPU clusters and cloud quantum computing resources.
Running the same hybrid workflow on IonQ’s trapped-ion architecture, following our recent demonstration on other quantum hardware, shows that Promethium’s approach to combining classical and quantum computing is not tied to a single type of quantum hardware.
Dr. Kin-Joe Sham, Co-Founder and COO at QC Ware
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