• 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.
• 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.
• 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."
Coherent Ising machines (CIMs) excel at solving large-scale combinational optimization problems (COPs), but their insufficient long-term stability has hindered their applications in compute-intensive tasks like computer-aided drug discovery (CADD). By improving fiber vibration isolation and temperature control system, we have implemented a 2000-node CIM named QBoson-CPQC-3Gen achieving stable solutions over one hour on large-scale COPs. Graph-based encoding schemes were further introduced to realize a CIM-based CADD workflow including allosteric site detection, protein-peptide docking and intermolecular similarity calculation. CIM-based methods demonstrated superior speed and accuracy than heuristic algorithms. Especially, QBoson-CPQC-3Gen identified 2 novel druggable sites and bioactive compounds for 6 targets, which were further validated in vitro, in-cell and by crystal structures. Our contributions established a quantum-computing framework for multi-stage drug discovery, representing a significant advancement in both quantum computing applications and pharmaceutical research.
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
The journey to develop drugs is long, time-consuming, and costly.Most drugs take years and billions of dollars before they hit the market.Traditional computations remain useful but have inadequacies to accurately reproduce molecular interactions and predict drug efficacy.Quantum computing represents a new tool to potentially revolutionize the challenges brought about by traditional computations in that it can more quickly and accurately solve several of these problems through applying the principles of quantum mechanics.This review examines the possibility of quantum computing to transform pharmaceutical research and drug development.The aim of this review is to give the reader an all-round perspective on the use of quantum computing in drug discovery, with its contributions to molecular modeling, drug-target interaction prediction, and pharmacological property optimization.It promises something new in the application of quantum algorithms to make up for the faults of classical methods used in pharmaceutical research.This review critically synthesizes a number of relevant studies that applied quantum computing capabilities to drug design.Particular attention is brought to quantum algorithms, including but not limited to VQE (Variational Quantum Eigensolver) and QAOA (Quantum Approximate Optimization Algorithm), in determining molecular structures in silico along with the capacity to predict chemical binding affinities.Secondly, the scope of the drugs-discovery application on the various platforms such as IBM Q versus Google Sycamore were compared with current classical computational approach.The quantum computer showed a lot of promise in the simulation of complicated molecular interactions faster than the classical counterparts.A lot of studies emphasize the accuracy quantum simulations have towards the prediction of drug-target binding mechanisms, and this makes the lead optimization process more efficient.However, with scalability and hardware issues, its application in the pharmaceutical research community is still yet to be wide-scale.Quantum computing promises to accelerate the drug development process by improving the speed and accuracy of molecular modeling and drug optimization.Although the field is still in its infancy, its integration into pharmaceutical research could lead to significant reductions in development time and cost.This review underlines the transformative potential of quantum computing and suggests future research directions for overcoming the current limitations and realizing its full potential in precision medicine.
A study published on the preprint repository arXiv expanded the existing system scale by more than 40 times by combining quantum embedding technology, which decomposes a molecule into multiple fragments, with a heterogeneous quantum-classical (HQC) computing method. Utilizing two 156-qubit quantum processors (ibm_cleveland, ibm_kobe) and two supercomputers (Fugaku, Miyabi-G), the research successfully simulated the quantum chemistry of protein-ligand complexes consisting of more than 12,000 atoms. The HQC method matched the accuracy level of the classical coupled-cluster (CCSD) method in fragment energies, proving its practical, industrial-scale potential to drastically reduce the time and cost of the drug discovery pipeline. [Quantum Biology Society] Predicting the interaction between a target protein and a drug candidate is crucial in drug discovery, but simulating its fundamental quantum mechanical structure could previously only be applied to extremely small-scale systems due to limitations in computational scale. However, a recent study successfully and accurately simulated complex protein-ligand complexes consisting of over 12,000 atoms through "heterogeneous quantum-classical (HQC) computing," which combines quantum computers with classical supercomputers, drawing the attention of the global pharmaceutical and IT industries. The study, "Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes," published on the preprint repository arXiv, was conducted by a multinational joint research team. This included Kenneth M. Merz, Jr.'s research team at the Cleveland Clinic Foundation in the U.S., Yuichi Otsuka and Tomonori Shirakawa's research teams at RIKEN in Japan, and researchers from IBM Quantum. ■ Splitting Molecules with 'Quantum Embedding' and Combining them with 'Supercomputers' The core of this massive calculation is "quantum embedding" technology. Instead of calculating the entire giant protein molecule at once, the method decomposes the molecule into multiple smaller fragments and simulates each independently. The research team executed 9,200 quantum circuits for over 100 hours, utilizing up to 94 qubits across two 156-qubit quantum processors (ibm_cleveland, ibm_kobe), and collected 1.3×10⁹ measurement outcomes. This is the most resource-intensive HQC computation performed in the field of quantum chemistry to date. Simultaneously, they mobilized Japan's world-class supercomputers, Fugaku and Miyabi-G, to perform subspace diagonalization via scalable distributed linear algebra kernels, achieving a high parallel efficiency of 72.5%. ■ System Size Expanded by 40x, Accuracy Improved by up to 210x Through this innovative workflow, the research team successfully simulated two giant protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (comprising 11,608 and 12,635 atoms, respectively). As a result, compared to previous state-of-the-art technologies, they achieved the feat of expanding the size of the simulatable system by more than 40 times and improving computational accuracy by up to 210 times. In particular, it is highly encouraging that the HQC method reached the high accuracy of the classical coupled-cluster (CCSD) level in fragment energy calculations. This achievement establishes a scalable pathway for systematically improvable biomolecular simulations, demonstrating the potential to drastically reduce the time and massive costs of the drug discovery pipeline through accurate characterization of drug-protein complexes. Experts evaluate this study as a crucial milestone showing that the technology to explore small-molecule hit substances for target proteins using actual quantum computers can be implemented on a real industrial scale, moving well beyond the laboratory level. https://arxiv.org/abs/2605.01138v1