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Convergence of Supercomputers and Quantum Computers Breaks the 12,000-Atom Barrier... A New Milestone in Drug Discovery

inquantio

Published Jul 13, 2026• 0 citations
Publisher

Abstract

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

DOI
10.5281/zenodo.21329274

Related papers

Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes

Kenneth M. Merz,, Akhil Shajan, Danil Kaliakin +21 more

We develop a workflow decomposing a molecule into fragments via quantum embedding and simulating them with a heterogeneous quantum-classical (HQC) method. We sample fragment electronic configurations on two 156-qubit quantum processors (ibm$\_$cleveland, ibm$\_$kobe), using up to 94 qubits, running 21,006 circuits for over 239 hours, collecting $3.0 \cdot 10^9$ measurement outcomes -- the most resource-intensive HQC computation for quantum chemistry to date. We compute fragment wavefunctions via optimized subspace diagonalization on supercomputers Fugaku, Miyabi-G, and ROQUO, achieving 72.5$\%$ parallel efficiency with scalable distributed linear algebra kernels. We simulate two protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (11,608, 12,635 atoms), demonstrate $>40\times$ increase in system size and up to $210\times$ improvement in accuracy over previous state-of-the-art, with HQC matching coupled-cluster (CCSD) accuracy in fragment energies. We present the first HQC protein-ligand binding prediction using a mixed-basis set and an automated end-to-end workflow enabling practical HQC calculations of large protein systems.

quant-pharXiv:2605.01138•May 5, 2026•3 citations

Overcoming the Computational Explosion of Drug Discovery with Quantum Computing

inquantio

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

•Jul 13, 2026•0 citations

Overcoming the Limits of Computational Cost... Emergence of a "Large-Scale Quantum Computing Framework" to Accelerate Drug Disc

inquantio

• 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.

•Jul 13, 2026•0 citations

Breaking the Bottleneck in Drug Screening... First Development of a Quantum Algorithm for Protein-Ligand Docking Identification

inquantio

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

•Jul 10, 2026•0 citations

Quantum-Centric Supercomputing and Photonic Bio-Quantum Architectures: An Exhaustive Evaluation of High-Fidelity Biomolecular Simulation and Mitochondrial Quantum Dynamics (Vol-II)

Syed Muntasir Mamun

This paper evaluates the breakthrough in computational biophysics marked by the successful ab initio quantum simulation of protein complexes up to 12,635 atoms, achieved through a heterogeneous Quantum-Centric Supercomputing (QCSC) paradigm. By leveraging advanced quantum processors (IBM Heron r2) and classical HPC (Miyabi-G) with the novel EWF-TrimSQD algorithm, researchers surpassed previous simulation size limits by over 40-fold, enabling accurate calculation of fragment energies and ligand-binding affinities for systems like Trypsin-benzamidine. Beyond drug Mamun, SM (2026) ……………………………….1 Quantum-Centric Supercomputing and Photonic Bio-Quantum Architectures: An Exhaustive Evaluation of High-Fidelity Biomolecular Simulation and Mitochondrial Quantum Dynamics (Vol-II) K S C discovery, this capacity provides the empirical foundation for rigorous investigation into quantum biology. The paper details non-classical phenomena, including proton tunneling in ribosomal peptide bond formation and the highly efficient electron/proton tunneling within the mitochondrial electron transport chain (ETC), highlighting how oxidative damage to these quantum pathways drives neurodegenerative pathology. Also available on Academia.edu: https://www.academia.edu/attachments/132704084/download_file?s=portfolio

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Molecular Quantum Computations on a Protein

Akhil Shajan, Danil Kaliakin, Fangchun Liang +7 more

This work presents the implementation of a fragment-based, quantum-centric supercomputing workflow for computing molecular electronic structure using quantum hardware. The workflow is applied to predict the relative energies of two conformers of the 300-atom Trp-cage miniprotein. The methodology employs wave function-based embedding (EWF) as the underlying fragmentation framework, in which all atoms in the system are explicitly included in the CI treatment. CI calculations for individual fragments are performed using either sample-based quantum diagonalization (SQD) for challenging fragments or full configuration interaction (FCI) for trivial fragments. To assess the accuracy of SQD for fragment CI calculations, EWF-(FCI,SQD) results are compared against EWF-MP2 and EWF-CCSD benchmarks. Overall, the results demonstrate that large-scale electronic configuration interaction (CI) simulations of protein systems containing hundreds or even thousands of atoms can be realized through the combined use of quantum and classical computing resources.

quant-pharXiv:2512.17130•Dec 22, 2025•0 citations
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
We develop a workflow decomposing a molecule into fragments via quantum embedding and simulating them with a heterogeneous quantum-classical (HQC) method. We sample fragment electronic configurations on two 156-qubit quantum processors (ibm_\__cleveland, ibm_\__kobe), using up to 94 qubits, running 21,006 circuits for over 239 hours, collecting 3.0⋅1093.0 \cdot 10^93.0⋅109 measurement outcomes -- the most resource-intensive HQC computation for quantum chemistry to date. We compute fragment wavefunctions via optimized subspace diagonalization on supercomputers Fugaku, Miyabi-G, and ROQUO, achieving 72.5%\%% parallel efficiency with scalable distributed linear algebra kernels. We simulate two protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (11,608, 12,635 atoms), demonstrate >40×>40\times>40× increase in system size and up to 210×210\times210× improvement in accuracy over previous state-of-the-art, with HQC matching coupled-cluster (CCSD) accuracy in fragment energies. We present the first HQC protein-ligand binding prediction using a mixed-basis set and an automated end-to-end workflow enabling practical HQC calculations of large protein systems.
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
• 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.
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
This paper evaluates the breakthrough in computational biophysics marked by the successful ab initio quantum simulation of protein complexes up to 12,635 atoms, achieved through a heterogeneous Quantum-Centric Supercomputing (QCSC) paradigm. By leveraging advanced quantum processors (IBM Heron r2) and classical HPC (Miyabi-G) with the novel EWF-TrimSQD algorithm, researchers surpassed previous simulation size limits by over 40-fold, enabling accurate calculation of fragment energies and ligand-binding affinities for systems like Trypsin-benzamidine. Beyond drug Mamun, SM (2026) ……………………………….1 Quantum-Centric Supercomputing and Photonic Bio-Quantum Architectures: An Exhaustive Evaluation of High-Fidelity Biomolecular Simulation and Mitochondrial Quantum Dynamics (Vol-II) K S C discovery, this capacity provides the empirical foundation for rigorous investigation into quantum biology. The paper details non-classical phenomena, including proton tunneling in ribosomal peptide bond formation and the highly efficient electron/proton tunneling within the mitochondrial electron transport chain (ETC), highlighting how oxidative damage to these quantum pathways drives neurodegenerative pathology. Also available on Academia.edu: https://www.academia.edu/attachments/132704084/download_file?s=portfolio
This work presents the implementation of a fragment-based, quantum-centric supercomputing workflow for computing molecular electronic structure using quantum hardware. The workflow is applied to predict the relative energies of two conformers of the 300-atom Trp-cage miniprotein. The methodology employs wave function-based embedding (EWF) as the underlying fragmentation framework, in which all atoms in the system are explicitly included in the CI treatment. CI calculations for individual fragments are performed using either sample-based quantum diagonalization (SQD) for challenging fragments or full configuration interaction (FCI) for trivial fragments. To assess the accuracy of SQD for fragment CI calculations, EWF-(FCI,SQD) results are compared against EWF-MP2 and EWF-CCSD benchmarks. Overall, the results demonstrate that large-scale electronic configuration interaction (CI) simulations of protein systems containing hundreds or even thousands of atoms can be realized through the combined use of quantum and classical computing resources.