A Quantum Framework for Protein Binding‐Site Structure Prediction on Utility‐Level Quantum Processors

This study presents a hybrid quantum‐classical framework for accurate prediction of protein structures on utility‐level quantum processors. We evaluate the practical application of the Variational Quantum Eigen‐solver (VQE) in protein structure prediction and demonstrate its superiority over state‐of‐the‐art deep learning methods in molecular docking accuracy and its potential implications for drug discovery and development. Abstract Accurate prediction of protein active‐site structures remains a central challenge in structural biology, especially for short and flexible peptide fragments where conventional and simulation‐based methods often fail. Here, we present a quantum computing framework designed for utility‐level quantum processors to address this problem. Starting from an amino acid sequence, we cast structure prediction as a ground‐state energy minimization task using the Variational Quantum Eigensolver (VQE). Amino acid connectivity is represented on a tetrahedral lattice, and steric, geometric, and chirality constraints are encoded into a problem‐specific Hamiltonian expressed as sparse Pauli operators. A two‐stage architecture separates energy estimation from measurement decoding, enabling noise mitigation under realistic device conditions. We evaluate the method on 23 real protein fragments from the PDBbind dataset and 7 fragments from therapeutically relevant proteins, executing all experiments on the IBM–Cleveland Clinic quantum processor. Structural predictions are benchmarked against AlphaFold3 (AF3) and classical simulation–based approaches using identical postprocessing and docking procedures. Our quantum framework outperforms both AF3 and classical baselines in Root‐Mean‐Square Deviation (RMSD) and docking efficacy, demonstrating a practical end‐to‐end pipeline for biologically relevant structure prediction on real quantum hardware and highlighting its engineering feasibility for near‐term quantum devices. This study presents a hybrid quantum-classical framework for accurate prediction of protein structures on utility-level quantum processors. We evaluate the practical application of the Variational Quantum Eigen-solver (VQE) in protein structure prediction and demonstrate its superiority over state-of-the-art deep learning methods in molecular docking accuracy and its potential implications for drug discovery and development. Abstract Accurate prediction of protein active-site structures remains a central challenge in structural biology, especially for short and flexible peptide fragments where conventional and simulation-based methods often fail. Here, we present a quantum computing framework designed for utility-level quantum processors to address this problem. Starting from an amino acid sequence, we cast structure prediction as a ground-state energy minimization task using the Variational Quantum Eigensolver (VQE). Amino acid connectivity is represented on a tetrahedral lattice, and steric, geometric, and chirality constraints are encoded into a problem-specific Hamiltonian expressed as sparse Pauli operators. A two-stage architecture separates energy estimation from measurement decoding, enabling noise mitigation under realistic device conditions. We evaluate the method on 23 real protein fragments from the PDBbind dataset and 7 fragments from therapeutically relevant proteins, executing all experiments on the IBM–Cleveland Clinic quantum processor. Structural predictions are benchmarked against AlphaFold3 (AF3) and classical simulation–based approaches using identical postprocessing and docking procedures. Our quantum framework outperforms both AF3 and classical baselines in Root-Mean-Square Deviation (RMSD) and docking efficacy, demonstrating a practical end-to-end pipeline for biologically relevant structure prediction on real quantum hardware and highlighting its engineering feasibility for near-term quantum devices. Advanced Science, EarlyView.