• 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.
• 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.
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.
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
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
• Constructed an integrated multi-stage drug design framework utilizing QBoson-CPQC-3Gen, a 2000-node coherent Ising machine (CIM). • Demonstrated superior speed and accuracy over existing heuristic algorithms in core calculations, including allosteric site detection, protein-peptide docking, and intermolecular similarity calculation. • Successfully identified and validated 2 novel druggable sites and bioactive compounds for 6 targets. [Quantum Biology Society] A recent study that overcomes the massive time and computational costs of drug discovery through quantum computing has captured the attention of the pharmaceutical and biotechnology industries. On February 11th, a paper titled "Large-scale quantum computing framework enhances drug discovery in multiple stages" was posted on bioRxiv, a preprint repository for the biological sciences. ■ Stable Computation for Over an Hour... Solving the CADD Bottleneck Traditional computer-aided drug discovery (CADD) consumes astronomical computational costs and resources when processing large-scale combinatorial optimization problems (COPs). To resolve this bottleneck, the research team improved the fiber vibration isolation and temperature control systems to introduce "QBoson-CPQC-3Gen," a coherent Ising machine (CIM) equipped with 2000 nodes. This system showcased groundbreaking performance by achieving stable optimal solutions on large-scale problems for over one hour. Utilizing this improved CIM, they successfully constructed a graph-based quantum computing framework capable of simultaneously handling three major challenges in drug discovery: allosteric site detection, protein-peptide docking, and intermolecular similarity calculation. ■ Overwhelming Speed and Accuracy, Complete with Real-World Wet-Lab Validation The experimental results were highly successful. To solve optimization problems, the newly developed CIM-based methods demonstrated superior computational speed and enhanced accuracy compared to the heuristic algorithms traditionally used on classical computers. Most notably, this technology did not stop at mere theoretical simulations. By applying this system to actual pharmaceutical research, the team successfully identified 2 novel druggable sites and bioactive compounds for 6 targets. Furthermore, these predictive results were comprehensively cross-validated in vitro, in-cell, and by crystal structures. By proving that quantum computing can be integrated into the entire multi-stage drug design process, this research represents a significant advancement in both quantum computing applications and pharmaceutical research.
We introduce an approach to sampling equilibrium solvent water molecule configurations within proteins that leverages analog quantum computing. We present a complete end-to-end study from the molecular biology application to the development of the quantum algorithm to the implementation on a neutral atom quantum processing unit (QPU). To do so, we combine a quantum placement strategy to the 3D Reference Interaction Site Model, an approach capable of predicting continuous solvent distributions. The intrinsic quantum nature of such coupling guarantees molecules not to be placed too close to each other, a constraint usually imposed by hand in classical approaches. We present first a full quantum adiabatic evolution model that uses a local Rydberg Hamiltonian to cast the general problem into an antiferromagnetic Ising model. Its solution is embodied into a Rydberg atom array QPU. Following a classical emulator implementation, a QPU portage allows to experimentally validate the algorithm performances on an actual quantum computer. As a perspective of use on next generation devices, we emulate a second hybrid quantum-classical version of the algorithm. Such a variational quantum approach uses a classical Bayesian minimization routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM algorithms open a route towards the application of analog quantum computing in molecular modeling and drug design. Published by the American Physical Society 2024
• Utilization of a 2000-node Coherent Ising Machine (CIM) achieves stable solutions for over an hour. • Resolves core challenges including allosteric site detection and peptide docking, demonstrating superior speed and accuracy over heuristic algorithms. • Efficacy of newly discovered compounds cross-validated through in vitro and in-cell assays, as well as crystal structures. [Quantum Biology Society] Quantum computing technology has presented a new breakthrough for the drug discovery process, which typically involves significant investments of time and resources. A revolutionary framework has been developed to support multi-stage drug design by leveraging quantum hardware to solve large-scale combinatorial optimization problems (COPs) frequently encountered in computer-aided drug discovery (CADD). According to recent academic reports, a study has been published demonstrating the application of a Coherent Ising Machine (CIM)—specialized in solving large-scale COPs—across an entire drug discovery pipeline. Despite their potential, earlier CIM implementations have struggled to sustain long-term stability, hindering their applications in compute-intensive tasks like CADD. The research team constructed a 2000-node, third-generation CIM named 'QBoson-CPQC-3Gen,' featuring improved fiber vibration isolation and temperature control systems. This system successfully overcame existing hardware limitations by achieving stable solutions over one hour on large-scale COPs. ■ Translating Biological Dilemmas into 'Quantum Language': Graph-Based Encoding A defining feature of this study is the establishment of an end-to-end framework capable of initiating from a disease protein and proceeding through a full drug-design pipeline. The researchers translated complex drug discovery problems into graph-based encoding schemes readable by the CIM. Through this approach, three core CADD tasks—allosteric site detection, protein-peptide docking, and intermolecular similarity calculation—were integrated into a unified quantum computing framework. ■ Demonstrating Search Speed and Accuracy that Outperform Existing Algorithms To overcome the limitations of conventional geometry-inspired local-search methods, the team reformulated allosteric site prediction as a global-search strategy to find the max-weighted clique within a graph. As a result, they identified two previously unreported binding sites on GALR1 and SIRT2 proteins, which are important targets for depression and aging, respectively. The framework also yielded remarkable results in peptide docking, which deals with flexible molecular conformations. Using a graph model weighted by binding energies, the team successfully derived novel peptide modulators acting on neurotensin receptor type 1 (NTSR1) and the APC-Asef protein-protein interaction, which is linked to colorectal cancer migration. Furthermore, intermolecular similarity calculations—essential for identifying "me-better" compounds—were resolved by converting the task into a maximum-weighted independent set (MWIS) problem that prevents invalid atom mappings. The CIM-based approach recorded overwhelming speedups, reducing computation time by one to five orders of magnitude compared to CPU-based heuristic algorithms. The research team rigorously validated the targets and compounds predicted by CIM computations through in vitro assays, in-cell experiments, and crystal structures. "This achievement is a perfect milestone demonstrating how quantum computing can pioneer real-world pharmaceutical research through concurrent advancements in both hardware and encoding methodologies," an industry expert commented. "It will dramatically accelerate the research and development (R&D) speed of platform biotech companies aiming to discover novel drug candidates and pursue out-licensing deals in the future."