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

TL;DR

This paper demonstrates the use of quantum computing to predict hydration sites in protein pockets, matching classical methods' accuracy and showing potential for drug discovery applications.

Contribution

It introduces a hybrid quantum-classical approach for protein hydration-site prediction, utilizing QUBO formulation and quantum optimization on NISQ hardware, advancing practical quantum applications in drug discovery.

Findings

Quantum approach reproduces experimental predictions.

Accuracy improves with more qubits, indicating scalability.

Potential advantages over classical methods in complex systems.

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.