Practical protein-pocket hydration-site prediction for drug discovery on a quantum computer
Abstract
Demonstrating the practical utility of Noisy Intermediate-Scale Quantum (NISQ) hardware for recurrent tasks in Computer-Aided Drug Discovery is of paramount importance. We tackle this challenge by performing three-dimensional protein pockets hydration-site prediction on a quantum computer. Formulating the water placement problem as a Quadratic Unconstrained Binary Optimization (QUBO), we use a hybrid approach coupling a classical three-dimensional reference-interaction site model (3D-RISM) 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 in reach. Finally, we provide evidence that advantageous situations could be found for systems where classical optimization struggles to provide optimal solutions. The method has potential for assisting simulations of protein-ligand complexes for drug lead optimization and setup of docking calculations.
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BibTeX
@article{loco2026practical,
title = {{Practical protein-pocket hydration-site prediction for drug discovery on a quantum computer}},
author = {Daniele Loco and Kisa Barkemeyer and Andre R. R. Carvalho and Jean-Philip Piquemal},
journal = {Physical Review Research},
year = {2026},
month = sep,
doi = {10.1103/gyqr-mv1h},
eprint = {2512.08390},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05408143v2},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Practical protein-pocket hydration-site prediction for drug discovery on a quantum computer
AU - Daniele Loco
AU - Kisa Barkemeyer
AU - Andre R. R. Carvalho
AU - Jean-Philip Piquemal
PY - 2026
DA - 2026/09/21
JO - Physical Review Research
DO - 10.1103/gyqr-mv1h
UR - https://hal.science/hal-05408143v2
AB - Demonstrating the practical utility of Noisy Intermediate-Scale Quantum (NISQ) hardware for recurrent tasks in Computer-Aided Drug Discovery is of paramount importance. We tackle this challenge by performing three-dimensional protein pockets hydration-site prediction on a quantum computer. Formulating the water placement problem as a Quadratic Unconstrained Binary Optimization (QUBO), we use a hybrid approach coupling a classical three-dimensional reference-interaction site model (3D-RISM) 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 in reach. Finally, we provide evidence that advantageous situations could be found for systems where classical optimization struggles to provide optimal solutions. The method has potential for assisting simulations of protein-ligand complexes for drug lead optimization and setup of docking calculations.
SN - 2643-1564
SN - 2643-1564
ER -