Practical protein-pocket hydration-site prediction for drug discovery on a quantum computer

Demonstrating the practical utility of noisy intermediate-scale quantum hardware for recurrent tasks in computer-aided drug discovery is of paramount importance. We tackle this challenge by performing three-dimensional hydration-site prediction in protein pockets on a quantum computer. Formulating the water placement problem as a quadratic unconstrained binary optimization, we use a hybrid approach coupling a classical three-dimensional reference-interaction site model to an efficient quantum optimization solver to run various hardware experiments up to 123 qubits. Matching the precision of classical approaches, our results reproduced experimental predictions on real-life protein-ligand complexes. Furthermore, through a detailed resource estimation analysis, we show that accuracy can be systematically improved with increasing number of qubits, indicating that full quantum utility is within reach as devices scale in the near term. The method has potential for assisting simulations of protein-ligand complexes for drug lead optimization and setup of docking calculations.