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.
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.
• Published on arXiv, successfully utilized actual quantum hardware with up to 123 qubits to predict hydration sites in three-dimensional protein pockets. • Formulated water placement as a Quadratic Unconstrained Binary Optimization (QUBO) problem, achieving precision matching classical computing by combining a classical 3D-RISM model with a quantum solver. • Confirmed that prediction accuracy systematically improves as the number of qubits increases, proving the practical utility of quantum computing in next-generation lead optimization and docking calculations. [Quantum Biology Society] In Computer-Aided Drug Discovery (CADD), accurately predicting how drugs and water molecules interact and are placed inside a target protein pocket—that is, the hydration site—plays a crucial role in evaluating drug binding affinity. However, long-standing questions have remained regarding whether current Noisy Intermediate-Scale Quantum (NISQ) hardware can solve such complex and recurrent biological optimization tasks at a practical level. Recently, a study tackling this limitation head-on by mobilizing an actual 123-qubit quantum computer was published on the preprint repository arXiv, drawing significant attention. This paper, titled "Practical protein-pocket hydration-site prediction for drug discovery on a quantum computer," shows that quantum computing can be deployed in earnest in the field of drug design. A joint research team including Dr. Daniele Loco from Qubit Pharmaceuticals in France, Professor Jean-Philip Piquemal from Sorbonne Université, and the quantum control company Q-CTRL achieved this groundbreaking result. ■ Replacing Water Placement with a Quantum Optimization Problem The research team newly formulated the optimal water placement problem inside a protein as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Instead of leaving the entire computation solely to quantum hardware, they adopted a hybrid approach coupling a classical three-dimensional reference-interaction site model (3D-RISM) with an efficient quantum optimization solver. Based on this, they performed various hardware experiments utilizing up to 123 qubits in an actual quantum hardware environment. As a result, they succeeded in reproducing a precision equivalent to state-of-the-art classical approaches in predicting the hydration sites of real-life protein-ligand complexes. ■ Evolving Accuracy as Qubits Increase Another significant achievement of this study is proving a distinct correlation between the increase in quantum resources (number of qubits) and prediction accuracy. Through a detailed resource estimation analysis, they confirmed that the accuracy of hydration-site prediction systematically improves as the number of mobilized qubits increases. This means that even in massive and complex biomolecular systems where classical optimization algorithms hit computational limits, quantum computers can reach full quantum utility as devices scale in the near term. This achievement strongly suggests that the quantum-classical hybrid hydration prediction model will become a groundbreaking tool that changes the landscape of next-generation lead optimization processes and precise drug docking calculations.
We present RinQ, a hybrid quantum-classical approach for identifying functionally critical residues in proteins by leveraging quantum computing techniques for optimization. Proteins, modeled as residue interaction networks, alongside the task of selecting top central residues, is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is then solved using D-Wave’s simulated annealing. Applied across a diverse set of proteins, the presented approach consistently identifies central residues aligned with classically calculated central sites. RinQ thus exemplifies the potential of near-term quantum and quantum-inspired methods to accelerate protein analysis and inform drug discovery. Our findings match classical benchmarks for small peptides and sets the stage for extending QUBO-based centrality detection to larger proteins using real quantum hardware.
A study published in the Journal of Computer-Aided Molecular Design presents the first innovative quantum algorithm for identifying the optimal docking sites where proteins and ligands bind during the drug design process. It expands the existing protein lattice model to fully include hydrogen bonding and hydrophobic interactions, introducing quantum state labelling for interaction sites and applying an extended and modified Grover quantum search algorithm. The algorithm was successfully verified on IBM's real 127-qubit quantum computers by applying error mitigation techniques (such as M3 and Pauli twirling), and it is expected to exponentially accelerate drug candidate screening for large proteins as hardware scales up in the future. [Quantum Biology Society] Even after the introduction of High-Performance Computers (HPC) into drug development, protein-ligand docking has faced fundamental computational limits with classical computing methods because it is inherently a complex quantum mechanical interaction process. In response to the question, "Can quantum computers be used in drug design and how?" raised alongside the rapid advancement of quantum computers today, a study titled "Quantum algorithm for protein-ligand docking sites identification in the interaction space," recently published in the international journal Journal of Computer-Aided Molecular Design, has provided a clear answer. An industry-academia joint research team consisting of Professor Ioannis G. Karafyllidis from the Democritus University of Thrace in Greece and researchers from the multinational pharmaceutical company Pfizer conducted this innovative study. ■ Combination of an Extended Lattice Model and Grover's Quantum Search Algorithm The researchers significantly expanded the previously used protein lattice model to fully encompass hydrogen bonding and hydrophobic interactions, which are key interactions between proteins and ligands. Within this expanded model, they introduced quantum state labelling for each interaction site and implemented an extended and modified form of Grover's quantum search algorithm. Simulation of the algorithm demonstrated that it can identify potential docking sites inside the target protein even within a massive interaction space, and evaluate the optimal binding positions much more efficiently and rapidly than classical computing tools through Euclidean distance calculations based on the quantum SWAP test. ■ Real Quantum Hardware Verification and Infinite Scalability Notably, this study did not stop at simulators but verified the algorithm's accuracy by running it directly on IBM's real 127-qubit quantum computers (such as ibm_brisbane). To overcome the hardware noise generated during this process, they successfully applied state-of-the-art quantum error mitigation techniques such as M3 (Matrix-free Measurement Mitigation), Pauli twirling, and dynamical decoupling. The most powerful asset of the newly developed quantum algorithm is its exceptional scalability. Thanks to this characteristic, the algorithm is perfectly suited for exploring binding sites within complex target proteins with massive molecular weights. By breaking the bottleneck of traditional drug discovery pipelines that required heavy computations, it is expected to serve as a revolutionary milestone that will exponentially explode the potential of drug candidate screening and molecular design as the number of available qubits increases with future hardware advancements. #QuantumAlgorithm #DrugScreening #ProteinLigandDocking #GroverAlgorithm #QuantumComputing #DrugDevelopment #MolecularDocking #QuantumBiology #Biophysics #KoreanQuantumBiologySociety https://www.researchgate.net/publication/393436574_Quantum_algorithm_for_protein-ligand_docking_sites_identification_in_the_interaction_space
A study published on the preprint repository arXiv comprehensively outlines empirical applications of quantum algorithms (such as VQE, QAOA) that can be executed on near-term noisy intermediate-scale quantum (NISQ) hardware to solve key challenges in molecular biology and drug discovery, such as protein folding and molecular docking. Through quantum simulations that overcome the limits of classical computing, it was demonstrated that by directly simulating the Hamiltonian of a chemical system, the 3D energy-minimized state of peptides or small molecule compounds can be accurately predicted with significantly fewer computing resources than before. It provides a practical roadmap for combining quantum machine learning (QML) and molecular biology to dramatically reduce massive computational costs and achieve overwhelming efficiency in the next-generation in silico drug discovery process. [Quantum Biology Society] Problems such as protein folding and molecular docking, which cause the biggest bottlenecks in the drug discovery process, become exponentially more complex as the amino acid sequence gets longer. The number of cases to explore explodes, making it difficult for classical computers to handle their NP-hard complexity. Recently, a groundbreaking demonstration framework aiming to introduce the latest quantum computing algorithms into the pharmaceutical and biotechnology fields to overcome these limitations has been presented, drawing attention from the related industry. The paper titled "Quantum computing for molecular biology," published on the preprint repository arXiv, comprehensively covers the application of practical quantum algorithms (e.g., VQE, QAOA) capable of solving drug discovery challenges that have hit the limits of classical computing, using noisy intermediate-scale quantum (NISQ) hardware. A research team including Alberto Baiardi and Markus Reiher from ETH Zurich in Switzerland, along with Matthias Christandl from the University of Copenhagen in Denmark, led this innovative convergent research. ■ Quantum Simulation: Overcoming the Limits of Classical Computing The research team demonstrated that by utilizing the characteristic of quantum computers to efficiently map and directly simulate the Hamiltonian of chemical systems, the complex 3D energy-minimized states of peptides or small molecule compounds can be accurately found with significantly fewer computing resources than before. This is because quantum computers can fundamentally bypass the "curse of dimensionality," where the computational amount expands exponentially, by naturally simulating the quantum mechanical properties of molecules. ■ A Roadmap for Next-Generation Drug Discovery This research goes beyond simply presenting algorithms; it provides a specific roadmap for next-generation in silico drug discovery processes by combining quantum machine learning (QML) and molecular biology. Through this, it has opened a path to achieve overwhelming efficiency by drastically reducing the massive computational costs and time required to screen drug candidates and optimize their most stable binding structures. This practical guide strongly suggests that quantum computing will become a core foundational technology bringing substantial innovation to the life sciences and pharmaceutical industries in the future. https://arxiv.org/abs/2212.12220
Accurate prediction of binding affinities and efficient lead optimization remain central challenges in computational drug discovery, particularly for complex biological targets characterized by rugged energy landscapes, conformational heterogeneity, and high-dimensional interaction spaces. Classical computational methods—such as molecular docking, force-field–based simulations, and deep learning models—have made substantial progress, yet they often struggle to capture quantum-mechanical effects and long-range correlations critical to molecular recognition. This study explores a quantum-enhanced framework that integrates Variational Quantum Algorithms (VQAs), quantum-inspired Hamiltonian modeling, and hybrid quantum–classical optimization workflows to improve the fidelity and scalability of drug discovery pipelines. By encoding molecular interactions into parameterized quantum circuits, the framework leverages quantum superposition and entanglement to explore complex chemical space more efficiently, enabling refined estimation of binding energies and accelerated identification of high-quality leads. Preliminary simulations indicate that VQA-based estimators outperform classical baselines for challenging protein–ligand systems, reducing prediction error while maintaining computational tractability. These findings highlight the emerging potential of quantum technologies to enable more accurate, data-efficient, and mechanistically grounded drug discovery for next-generation therapeutics.
Over the past two decades, the development of novel drugs evolved into a high-demanding computational field. There is a constant and increasing need for advanced methods for determining protein-ligand binding in the drug design process. Even after the introduction and use of High-Performance Computers in drug design, fundamental problems and constraints have not been dealt with in a satisfactory manner. This is partially due to the fact that ligand docking in proteins is a quantum mechanical process. With the quantum computers available today, the question "Can quantum computers be used in drug design and how?" arises naturally. A novel quantum algorithm for protein-ligand docking site identification is presented here. In detail, the protein lattice model has been expanded to include protein-ligand interactions. Quantum state labelling for the interaction sites is introduced, and an extended and modified Grover quantum search algorithm is implemented to search for docking sites. This algorithm has been tested and executed on both a quantum simulator and a real quantum computer. The results show that the quantum algorithm can identify effectively docking sites. The quantum algorithm is highly scalable and well-suited for identifying docking sites within large proteins, poised to harness the potential of increased quantum bits in the future.