Quantum computational quantification of protein–ligand interactions
Open accessAbstract
Abstract We have demonstrated a prototypical hybrid classical and quantum computational workflow for the quantification of protein–ligand interactions. The workflow combines the density matrix embedding theory (DMET) embedding procedure with the variational quantum eigensolver (VQE) approach for finding molecular electronic ground states. A series of ‐secretase (BACE1) inhibitors is rank‐ordered using binding energy differences calculated on the latest superconducting transmon (IBM) and trapped‐ion (Quantinuum) noisy intermediate scale quantum (NISQ) devices. This is the first application of real quantum computers to the calculation of protein‐ligand binding energies. The results shed light on hardware and software requirements which would enable the application of NISQ algorithms in drug design.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Quantum computing can contribute to biochemical systems, biology, biochemistry, computation of protein-ligand interplay, mRNA codon optimization, and protein folding with higher-order speeds compared with conventional computers.
“its contribution is possible in biochemical systems [7], biology [8], biochemistry [9], computation of protein-ligand interplay [10], mRNA codon optimization [11], and protein folding [12] to name a few with higherorder speeds compared with conventional computers.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [10]
The Abstract (p. 1) describes a quantum–classical workflow that calculates protein–ligand binding energies, supporting the part attached to citation [10]. The paper does not establish higher computational speeds than conventional computers; the Discussion (p. 10) instead describes current hardware limitations, including relatively high gate times. (LLM, conf=0.88)
Experimental — accuracy not yet measuredVQE-based methods have delivered data for parametrizing classical force fields.
“This idea has been recently explored in Refs.[190] and [191], where VQE-based methods delivered data for parametrizing classical force fields.”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [191]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J., Impey, R., & Klein, M. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 38,335 · see citing works
Zhao, Y., & Truhlar, D. G. (2007) · Theoretical Chemistry Accounts · doi:10.1007/s00214-007-0310-x · Cited by 30,407 · see citing works
Ditchfield, R., Hehre, W. J., & Pople, J. A. (1971) · The Journal of Chemical Physics · doi:10.1063/1.1674902 · Cited by 10,612 · see citing works
A variational eigenvalue solver on a photonic quantum processor
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014) · Nature Communications · doi:10.1038/ncomms5213 · Cited by 4,552 · see citing works
Hehre, W. J., Stewart, R. F., & Pople, J. A. (1969) · The Journal of Chemical Physics · doi:10.1063/1.1672392 · Cited by 4,227 · see citing works
Systematic optimization of long-range corrected hybrid density functionals
Chai, J., & Head‐Gordon, M. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2834918 · Cited by 4,063 · see citing works
Density-functional thermochemistry. I. The effect of the exchange-only gradient correction
Becke, A. D. (1992) · The Journal of Chemical Physics · doi:10.1063/1.462066 · Cited by 3,939 · see citing works
Coupled-cluster theory in quantum chemistry
Bartlett, R. J., & Musiał, M. (2007) · Reviews of Modern Physics · doi:10.1103/revmodphys.79.291 · Cited by 3,343 · see citing works
Molecular Electronic‐Structure Theory
Helgaker, T., Jørgensen, P., & Olsen, J. (2000) · doi:10.1002/9781119019572 · Cited by 2,594 · see citing works
Goerigk, L., Hansen, A., Bauer, C. A., Ehrlich, S., Najibi, A., & Grimme, S. (2017) · Physical Chemistry Chemical Physics · doi:10.1039/c7cp04913g · Cited by 1,898 · see citing works
Dispersion-Corrected Mean-Field Electronic Structure Methods
Grimme, S., Hansen, A., Brandenburg, J. G., & Bannwarth, C. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00533 · Cited by 1,632 · see citing works
Quantum computational chemistry
McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S. C., & Yuan, X. (2020) · Reviews of Modern Physics · doi:10.1103/revmodphys.92.015003 · Cited by 1,621 · see citing works
Quantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., Kivlichan, I. D., Menke, T., Peropadre, B., Sawaya, N. P. D., et al. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00803 · Cited by 1,560 · see citing works
Molecular electronic-structure theory
Ratner, M. A. (2000) · Cited by 1,506 · see citing works
Qiskit: An Open-source Framework for Quantum Computing
Aleksandrowicz, G., Alexander, T., Barkoutsos, P. K., Bello, L., Ben-Haim, Y., Bucher, D., Cabrera-Hernández, F. J., Carballo-Franquis, J., Chen, A., Chen, C.-F., et al. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.2562110 · Cited by 1,454 · see citing works
Qiskit: An Open-source Framework for Quantum Computing
Aleksandrowicz, G., Alexander, T., Barkoutsos, P. K., et al. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.2562111 · Cited by 1,314 · see citing works
Quantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J., Degroote, M., Johnson, P., Kieferová, M., Kivlichan, I., Menke, T., Peropadre, B., Sawaya, N., et al. (n.d.) · OPUS - Open Publications of UTS Scholars (University of Technology Sydney) · Cited by 1,265 · see citing works
Recent developments in the P <scp>y</scp> SCF program package
Sun, Q., Zhang, X., Banerjee, S., Bao, P., Barbry, M., Blunt, N. S., Bogdanov, N. A., Booth, G. H., Chen, J., Cui, Z.-H., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0006074 · Cited by 1,259 · see citing works
Quantum Algorithms for Quantum Chemistry and Quantum Materials Science
Bauer, B., Bravyi, S., Motta, M., & Chan, G. K.-L. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00829 · Cited by 848 · see citing works
Towards quantum chemistry on a quantum computer
Lanyon, B. P., Whitfield, J. D., Gillett, G. G., Goggin, M. E., Almeida, M. P., Kassal, I., Biamonte, J. D., Mohseni, M., Powell, B. J., Barbieri, M., et al. (2010) · Nature Chemistry · doi:10.1038/nchem.483 · Cited by 796 · see citing works
On the Nonorthogonality Problem
Löwdin, P. (1970) · Advances in quantum chemistry · doi:10.1016/s0065-3276(08)60339-1 · Cited by 782 · see citing works
Cournia, Z., Allen, B. K., & Sherman, W. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00564 · Cited by 725 · see citing works
Motta, M., Sun, C., Tan, A. T. K., O’Rourke, M. J., Ye, E., Minnich, A. J., Brandão, F. G. S. L., & Chan, G. K.-L. (2020) · Nature Physics · doi:10.1038/s41567-019-0704-4 · Cited by 709 · see citing works
Protonate3D: Assignment of ionization states and hydrogen coordinates to macromolecular structures
Labute, P. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22234 · Cited by 570 · see citing works
Labute, P. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.20933 · Cited by 500 · see citing works
t|ket⟩: a retargetable compiler for NISQ devices
Sivarajah, S., Dilkes, S., Cowtan, A., Simmons, W., Edgington, A., & Duncan, R. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/ab8e92 · Cited by 475 · see citing works
Marenich, A. V., Olson, R. M., Kelly, C. P., Cramer, C. J., & Truhlar, D. G. (2007) · Journal of Chemical Theory and Computation · doi:10.1021/ct7001418 · Cited by 469 · see citing works
Variational Quantum Computation of Excited States
Higgott, O., Wang, D., & Brierley, S. (2019) · Quantum · doi:10.22331/q-2019-07-01-156 · Cited by 463 · see citing works
Generalized Unitary Coupled Cluster Wave functions for Quantum Computation
Lee, J., Huggins, W. J., Head-Gordon, M., & Whaley, K. B. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b01004 · Cited by 432 · see citing works
BACE1 (β-secretase) inhibitors for the treatment of Alzheimer's disease
Ghosh, A. K., & Osswald, H. L. (2014) · Chemical Society Reviews · doi:10.1039/c3cs60460h · Cited by 361 · see citing works
Density Matrix Embedding: A Strong-Coupling Quantum Embedding Theory
Knizia, G., & Chan, G. K. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct301044e · Cited by 360 · see citing works
Mitigating measurement errors in multiqubit experiments
Bravyi, S., Sheldon, S., Kandala, A., Mckay, D. C., & Gambetta, J. M. (2021) · Physical Review A · doi:10.1103/physreva.103.042605 · Cited by 343 · see citing works
Automated Selection of Active Orbital Spaces
Stein, C. J., & Reiher, M. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00156 · Cited by 321 · see citing works
Baiardi, A., & Reiher, M. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5129672 · Cited by 306 · see citing works
Liakos, D. G., Guo, Y., & Neese, F. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b05734 · Cited by 304 · see citing works
Qulacs: a fast and versatile quantum circuit simulator for research purpose
Suzuki, Y., Kawase, Y., Masumura, Y., Hiraga, Y., Nakadai, M., Chen, J., Nakanishi, K. M., Mitarai, K., Imai, R., Tamiya, S., et al. (2021) · Quantum · doi:10.22331/q-2021-10-06-559 · Cited by 303 · see citing works
Geometry optimization made simple with translation and rotation coordinates
Wang, L., & Song, C. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4952956 · Cited by 291 · see citing works
A Practical Guide to Density Matrix Embedding Theory in Quantum Chemistry
Wouters, S., Jiménez-Hoyos, C. A., Sun, Q., & Chan, G. K. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00316 · Cited by 255 · see citing works
Automated Construction of Molecular Active Spaces from Atomic Valence Orbitals
Sayfutyarova, E. R., Sun, Q., Chan, G. K., & Knizia, G. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00128 · Cited by 233 · see citing works
Noisy intermediate-scale quantum (NISQ) algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2021) · arXiv · doi:10.48550/arxiv.2101.08448 · Cited by 166 · see citing works
The ONETEP linear-scaling density functional theory program
Prentice, J. C. A., Aarons, J., Womack, J. C., Allen, A. E. A., Ανδρινόπουλος, Λ., Anton, L., Bell, R. A., Bhandari, A., Bramley, G. A., Charlton, R. J., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0004445 · Cited by 157 · see citing works
Overview of the SAMPL6 host–guest binding affinity prediction challenge
Rizzi, A., Murkli, S. L., McNeill, J. N., Yao, W., Sullivan, M., Gilson, M. K., Chiu, M. W., Isaacs, L., Gibb, B. C., Mobley, D. L., et al. (2018) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-018-0170-6 · Cited by 144 · see citing works
Hilpert, H., Guba, W., Woltering, T., Wostl, W., Pinard, E., Mauser, H., Mayweg, A., Rogers-Evans, M., Humm, R., Krummenacher, D., et al. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm400225m · Cited by 119 · see citing works
Self-citationLinear‐scaling self‐consistent field methods for large molecules
Kußmann, J., Beer, M., & Ochsenfeld, C. (2013) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1138 · Cited by 119 · see citing works
Fast Domain Decomposition Algorithm for Continuum Solvation Models: Energy and First Derivatives
Lipparini, F., Stamm, B., Eric Cancès, B., Maday, Y., & Mennucci, B. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400280b · Cited by 117 · see citing works
Gokhale, P., Angiuli, O., Ding, Y., Gui, K., Tomesh, T., Suchara, M., Martonosi, M., & Chong, F. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.13623 · Cited by 113 · see citing works
Application of Free Energy Perturbation for the Design of BACE1 Inhibitors
Ciordia, M., Pérez‐Benito, L., Delgado, F., Trabanco, A. A., & Tresadern, G. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00220 · Cited by 105 · see citing works
Ratcliff, L. E., Dawson, W., Fisicaro, G., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0004792 · Cited by 104 · see citing works
Amezcua, M., Khoury, L. E., & Mobley, D. L. (2021) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00363-5 · Cited by 80 · see citing works
Quantum computation of dominant products in lithium–sulfur batteries
Rice, J. E., Gujarati, T. P., Motta, M., Takeshita, T. Y., Lee, E., Latone, J. A., & Garcia, J. M. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0044068 · Cited by 77 · see citing works
Motta, M., Gujarati, T. P., Rice, J. E., Kumar, A., Masteran, C., Latone, J. A., Lee, E., Valeev, E. F., & Takeshita, T. Y. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp04106h · Cited by 76 · see citing works
Lipparini, F., Scalmani, G., Lagardère, L., Stamm, B., Eric Cancès, B., Maday, Y., Piquemal, J., Frisch, M. J., & Mennucci, B. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4901304 · Cited by 75 · see citing works
(2020) · Springer eBooks · doi:10.1007/978-981-15-6815-2 · Cited by 70 · see citing works
Accurate many-body electronic structure near the basis set limit: Application to the chromium dimer
Li, J., Yao, Y., Holmes, A. A., et al. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.012015 · Cited by 49 · see citing works
Gundelach, L., Fox, T., Tautermann, C. S., & Skylaris, C. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp00206f · Cited by 49 · see citing works
A Generic Compilation Strategy for the Unitary Coupled Cluster AnsatzPreprint
Cowtan, A., Simmons, W., & Duncan, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10515 · Cited by 49 · see citing works
Kottmann, J. S., Schleich, P., Tamayo-Mendoza, T., & Aspuru‐Guzik, A. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c03410 · Cited by 48 · see citing works
Towards the simulation of large scale protein–ligand interactions on NISQ-era quantum computers
Malone, F. D., Parrish, R. M., Welden, A. R., Fox, T., Degroote, M., Kyoseva, E., Moll, N., Santagati, R., & Streif, M. (2022) · Chemical Science · doi:10.1039/d1sc05691c · Cited by 48 · see citing works
Exact and approximate symmetry projectors for the electronic structure problem on a quantum computer
Yen, T.-C., Lang, R. A., & Izmaylov, A. F. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5110682 · Cited by 40 · see citing works
(2018) · Advanced Quantum Technologies · doi:10.1002/(issn)2511-9044 · Cited by 39 · see citing works
Detecting correlated errors in state-preparation-and-measurement tomography
Jackson, C. S., & van Enk, S. J. (2015) · Physical Review A · doi:10.1103/physreva.92.042312 · Cited by 38 · see citing works
Free Energy Calculations for Protein–Ligand Binding Prediction
Jespers, W., Åqvist, J., & Gutiérrez‐de‐Terán, H. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1209-5_12 · Cited by 35 · see citing works
Aydeniz, K. Y., Gard, B., Jakowski, J., Majumder, S., Barron, G. S., Siopsis, G., Humble, T. S., & Pooser, R. C. (2021) · Advanced Quantum Technologies · doi:10.1002/qute.202100012 · Cited by 33 · see citing works
Yamazaki, T., Matsuura, S., Narimani, A., Saidmuradov, A., & Zaribafiyan, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.01305 · Cited by 31 · see citing works
Small-molecule BACE1 inhibitors: a patent literature review (2011 to 2020)
Rombouts, F. J. R., Kusakabe, K., Hsiao, C., & Gijsen, H. J. M. (2020) · Expert Opinion on Therapeutic Patents · doi:10.1080/13543776.2021.1832463 · Cited by 31 · see citing works
Protein-Ligand Interactions and Drug Design
(2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1209-5 · Cited by 28 · see citing works
Beck, M. E., Riplinger, C., Neese, F., et al. (2020) · Journal of Computational Chemistry · doi:10.1002/jcc.26454 · Cited by 27 · see citing works
Potency Prediction of β-Secretase (BACE-1) Inhibitors Using Density Functional Methods
Roos, K., Viklund, J., Meuller, J., et al. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400374z · Cited by 22 · see citing works
Towards the Simulation of Large Scale Protein-Ligand Interactions on NISQ-era Quantum ComputersPreprint
Malone, F. D., Parrish, R. M., Welden, A. R., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.01589 · Cited by 1 · see citing works
Honeywell and Cambridge Quantum Reach New Milestones
Tyler, N. (2021) · New Electronics · doi:10.12968/s0047-9624(22)60305-5 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Dirac P. A. M., 1929, Proceedings of the Royal Society of London, Series A. Containing Papers of a Mathematical and Physical Character, 714
- H. Liu G. H. Low D. S. Steiger T. Häner M. Reiher M. Troyer arXiv preprint: arXiv:2102.10081 2021. · arXiv
- Y. S. Yordanov V. Armaos C. H. W. Barnes D. R. M. Arvidsson‐Shukur arXiv preprint: arXiv:2011.10540 2020. · arXiv
- K. Yamamoto D.Z. Manrique I. Khan H. Sawada D. M. Ramo arXiv preprint: arXiv:2109.08401 2021. · arXiv
- W. Kirby B. Fuller C. Hadfield A. Mezzacapo arXiv preprint: arXiv:2109.14465 2021. · arXiv
Cited by
Sorted by citations
The Variational Quantum Eigensolver: A review of methods and best practices
Tilly, J., Chen, H., Cao, S., Picozzi, D., Setia, K., Li, Y., Grant, E., Wossnig, L., Rungger, I., Booth, G. H., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2022.08.003 · Cited by 1,031 · see citing works
Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Tropsha, A., Isayev, O., Varnek, A. A., Schneider, G., & Cherkasov, A. R. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00832-0 · Cited by 423 · see citing works
“…drugs, the study of transition states and coordinates of enzymatic reactions, the development of quantum chemical descriptors to empower QSAR modelling and the computation of accurate thermodynamics of drug–target interactions 131 , and supporting other innovations in pharmaceutical discovery 128 .”
Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications
Blunt, N. S., Camps, J., Crawford, O., Izsák, R., Leontica, S., Mirani, A., Moylett, A. E., Scivier, S. A., Sünderhauf, C., Schopf, P., et al. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00574 · Cited by 192 · see citing works
The state of quantum computing applications in health and medicine
Flöther, F. F. (2023) · Research Directions Quantum Technologies · doi:10.1017/qut.2023.4 · Cited by 128 · see citing works
Quantum computing for near-term applications in generative chemistry and drug discovery.
Pyrkov, A., Aliper, A., Bezrukov, D., Lin, Y.-C., Polykovskiy, D., Kamya, P., Ren, F., & Zhavoronkov, A. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103675 · Cited by 100 · see citing works
Quantum Computing for Molecular Biology**
Baiardi, A., Christandl, M., & Reiher, M. (2023) · ChemBioChem · doi:10.1002/cbic.202300120 · Cited by 93 · see citing works
Pal, S., Bhattacharya, M., Lee, S.-S., & Chakraborty, C. (2024) · Molecular Biotechnology · doi:10.1007/s12033-023-00765-4 · Cited by 84 · see citing works
“…in biological interactions, its contribution is possible in biochemical systems [7], biology [8], biochemistry [9], computation of protein–ligand interplay [10], mRNA codon optimization [11], and protein folding [12] to name a few with higher-order speeds compared with conventional computers.”
Orbital-optimized pair-correlated electron simulations on trapped-ion quantum computers
Zhao, L., Goings, J., Shin, K., Kyoung, W., Fuks, J. I., Kevin Rhee, J.-K., Rhee, Y. M., Wright, K., Nguyen, J., Kim, J., et al. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00730-8 · Cited by 73 · see citing works
“[30] Josh JM Kirsopp, Cono Di Paola, David Zsolt Manrique, Michal Krompiec, Gabriel Greene-Diniz, Wolfgang Guba, Agnes Meyder, Detlef Wolf, Martin Strahm, and David Muñoz Ramo, “Quantum computational quantification of protein–ligand interactions,” International Journal of Quantum Chemistry 122, e26975 (2022).”
BackgroundQuantum embedding theories to simulate condensed systems on quantum computers
Vorwerk, C., Sheng, N., Govoni, M., Huang, B., & Galli, G. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00279-0 · Cited by 65 · see citing works
Quantum Embedding Method for the Simulation of Strongly Correlated Systems on Quantum Computers
Rossmannek, M., Pavošević, F., Rubio, Á., & Tavernelli, I. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c00330 · Cited by 58 · see citing works
Efficient quantum computation of molecular forces and other energy gradients
O’Brien, T. E., Streif, M., Rubin, N. C., Santagati, R., Su, Y., Huggins, W. J., Goings, J. J., Moll, N., Kyoseva, E. S., Degroote, M., et al. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.043210 · Cited by 56 · see citing works
Verifiable Blind Quantum Computing with Trapped Ions and Single Photons
Drmota, P., Nadlinger, D. P., Main, D., Nichol, B. C., Ainley, E. M., Leichtle, D., Mantri, A., Kashefi, E., Srinivas, R., Araneda, G., et al. (2024) · Physical Review Letters · doi:10.1103/physrevlett.132.150604 · Cited by 51 · see citing works
“Cloud-based approaches, where users can remotely access quantum servers, are likely to be the working model in the near term and beyond; many users already perform computations on commercially available devices for state-of-the-art research [1–5].”
BackgroundTowards practical and massively parallel quantum computing emulation for quantum chemistry
Shang, H., Fan, Y., Shen, L., Guo, C., Liu, J., Duan, X., Li, F., & Li, Z. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00696-7 · Cited by 46 · see citing works
“After that, the energy differences between bounded and unbounded geometries of ligands are estimated with DMET-VQE [30].”
MethodMolecular docking via quantum approximate optimization algorithm
Ding, Q.-M., Huang, Y., & Yuan, X. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.21.034036 · Cited by 48 · see citing works
“Score function-based approaches involve scoring functions to evaluate docking poses, as demonstrated in recent studies [20, 21].”
A primer for quantum computing and its applications to healthcare and biomedical research
Durant, T. J. S., Knight, E., Nelson, B. G., Dudgeon, S. N., Lee, S. J., Walliman, D., Young, H. P., Lucila Ohno-machado, A., & Schulz, W. L. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae149 · Cited by 41 · see citing works
Future Potential of Quantum Computing and Simulations in Biological Science
Pal, S., Bhattacharya, M., Dash, S., Lee, S., & Chakraborty, C. (2023) · Molecular Biotechnology · doi:10.1007/s12033-023-00863-3 · Cited by 39 · see citing works
“In this regard, quantum computational techniques can answer complicated biological problems which require fast and numerous computations [1–7].”
Quantum computing research in medical sciences
Alrashed, S., & Min‐Allah, N. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101606 · Cited by 40 · see citing works
Large-Scale Simulation of Quantum Computational Chemistry on a New Sunway Supercomputer
Shang, H., Li, S., Fan, Y., Xu, Z., Guo, C., Liu, J., Zhou, W., Ma, H., Lin, R., Yang, Y., et al. (2022) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41404.2022.00019 · Cited by 43 · see citing works
“Here by using the ”frozen protein” approximation [55] to remove the protein-protein interactions, we use Eb = Eligand in protein − Eligand to get the binding energy Eb, which is then used as the metric for ranking.”
MethodModelling carbon capture on metal-organic frameworks with quantum computing
Greene‐Diniz, G., Manrique, D. Z., Sennane, W., Magnin, Y., Shishenina, E., Cordier, P., Llewellyn, P. L., Krompiec, M., Rančić, M. J., & Ramo, D. M. (2022) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-022-00155-w · Cited by 35 · see citing works
“We note that SPAM mitigation and PMSV can lead to different results when calculating E.”
MethodInfluentialSelf-citationPeptide conformational sampling using the Quantum Approximate Optimization Algorithm
Boulebnane, S., Lucas, X., Meyder, A., Adaszewski, S., & Montanaro, A. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00733-5 · Cited by 32 · see citing works
“There exists a significant body of research on applying quantum algorithms to protein dynamics [6, 47, 28, 7, 51, 3, 38, 42].”
BackgroundSelf-citationLim, H., Kang, D. H., Kim, J., Pellow-Jarman, A., McFarthing, S., Pellow-Jarman, R., Jeon, H.-N., Oh, B., Rhee, J. K., & No, K. T. (2024) · Scientific Reports · doi:10.1038/s41598-024-52926-3 · Cited by 31 · see citing works
“These methods were designed to algorithmi-cally complement the limited qubit capacity of NISQ devices, thereby broadening their scope, even in investigating protein–ligand interactions for drug design 40 .”
MethodEffective Reconstruction of Expectation Values from Ab Initio Quantum Embedding
Nusspickel, M., Ibrahim, B., & Booth, G. H. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01063 · Cited by 28 · see citing works
Mokhtari, M., Khoshbakht, S., Ziyaei, K., Akbari, M. E., & Moravveji, S. S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae074 · Cited by 24 · see citing works
“It’s important to highlight that this research draws upon quantum biological data and leverages a quantum computer platform [185].”
BackgroundShajan, A., Kaliakin, D. S., Mitra, A., Moreno, J. R., Li, Z., Motta, M., Johnson, C., Saki, A. A., Das, S., Sitdikov, I., et al. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00114 · Cited by 28 · see citing works
TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era
Li, W., Allcock, J., Cheng, L., Zhang, S., Chen, Y., Mailoa, J. P., Shuai, Z., & Zhang, S. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00319 · Cited by 18 · see citing works
The Future of Drug Development with Quantum Computing
Bonde, B. K., Patil, P., & Choubey, B. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3449-3_7 · Cited by 19 · see citing works
Protein recognition methods for diagnostics and therapy
Montoya, R., Deckerman, P., & Guler, M. O. (2025) · BBA Advances · doi:10.1016/j.bbadva.2025.100149 · Cited by 17 · see citing works
“With the increased applicability of quantum computing, a growing number of researchers are interested in leveraging the computational power of quantum computers to better understand potentially pharmaceutically-relevant compounds like β -secretase inhibitors [34,35].”
Weaving, T., Ralli, A., Kirby, W. M., Love, P. J., Sauro Succi, A., & Coveney, P. V. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.043054 · Cited by 13 · see citing works
Loipersberger, M., Malone, F. D., Welden, A. R., Parrish, R. M., Fox, T., Degroote, M., Kyoseva, E. S., Moll, N., Santagati, R., & Streif, M. (2023) · Chemical Science · doi:10.1039/d2sc05896k · Cited by 17 · see citing works
Quantum-machine-assisted drug discovery
Zhou, Y., Chen, J., Cheng, J., Cao, X., Zhang, Y., Karemore, G., Žitnik, M., Chong, F. T., Liu, J., Fu, T., et al. (2026) · npj Drug Discovery. · doi:10.1038/s44386-025-00033-2 · Cited by 19 · see citing works
“…in handling high-dimensional data spaces, first demonstrated in particle physics applications [134], has particular relevance for clinical trial recruitment, where the integration of genomic, clinical, and imaging data creates computational challenges that exceed classical capabilities [153, 154].”
Implementation of the Projective Quantum Eigensolver on a Quantum Computer
Misiewicz, J. P., & Evangelista, F. A. (2024) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c07429 · Cited by 11 · see citing works
Gundelach, L., Fox, T., Tautermann, C. S., et al. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp03705j · Cited by 10 · see citing works
Sun, J., Cheng, L., & Li, W. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00886 · Cited by 10 · see citing works
Quantum computing: current and potential applications in digital agriculture
de Souza, K. X. S., Bolfe, É. L., Leite, M. A. D. A., Bambini, M. D., Visoli, M. C., Júnior, A. L., da Silva, F. R., César, J., & Yassitepe, J. É. D. C. T. (2024) · Pesquisa Agropecuária Brasileira · doi:10.1590/s1678-3921.pab2024.v59.03753 · Cited by 10 · see citing works
Review of quantum algorithms for medicine, finance and logistics
Ciacco, A., Guerriero, F., & Macrina, G. (2025) · Soft Computing · doi:10.1007/s00500-025-10540-z · Cited by 12 · see citing works
“Robert et al. (2021) present a VQE to solve the folding of a polymer chain on a lattice, while Kirsopp et al. (2022) present a VQE to find the fundamental states of molecular electronics.”
HINT, a code for understanding the interaction between biomolecules: a tribute to Donald J. Abraham
Kellogg, G. E., Marabotti, A., Spyrakis, F., & Mozzarelli, A. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1194962 · Cited by 7 · see citing works
“This is a quite challenging goal that has yet to be met, except in toy systems (Yilmazer and Korth, 2016; Cavasotto and Aucar, 2020; Kirsopp et al., 2021).”
BackgroundS, B. G. A., Madaj, R., Sanker, A., Valluri, P. P., & S, B. G. A. (2022) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.2024450 · Cited by 6 · see citing works
Early quantum computing applications on the path towards precision medicine
Flöther, F. F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-98808-7.00001-1 · Cited by 7 · see citing works
Kumar, S., Misra, S. K., Tiwari, A., Katiyar, A., Awasthi, D. A., Singh, S. K., Katiyar, A., Dhawan, A., & Kumar, A. (2026) · Talanta · doi:10.1016/j.talanta.2026.129802 · Cited by 5 · see citing works
Extending quantum computing through subspace, embedding and classical molecular dynamics techniques
Bickley, T. M., Mingare, A., Weaving, T., de la Bastida, M. W., Wan, S., Nibbi, M., Seitz, P., Ralli, A., Love, P. J., Chung, M. T., et al. (2025) · Digital Discovery · doi:10.1039/d5dd00225g · Cited by 5 · see citing works
Quantum medicine: A quantum–mechanical framework for redox biology, disease and precision medicine
Sung, J., & Cheong, J. (2026) · Clinical and Translational Medicine · doi:10.1002/ctm2.70598 · Cited by 5 · see citing works
Imaginary Hamiltonian variational Ansatz for combinatorial optimization problems
Wang, X., Chai, Y., Feng, X., Guo, Y., Jansen, K., & Tüysüz, C. (2025) · Physical Review A · doi:10.1103/physreva.111.032612 · Cited by 4 · see citing works
Molecular Quantum Computations on a Protein
Shajan, A., Kaliakin, D. S., Liang, F., Pellegrini, T., Doğa, H., Bhowmik, S., Das, S., Mezzacapo, A., Motta, M., & Merz, K. M. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00364 · Cited by 3 · see citing works
Wang, Z., Zhou, H., Chen, G.-L., Yilong, A., Chun-huo, H., Lu, Y., Tang, K.-H., Du, L., Hua-bin, Z., & Huang, X.-Q. (2008) · PubMed · Cited by 3 · see citing works
Network-level control of G protein-coupled receptors: a panacea for chronic diseases?
Boeringer, T., Pardo, M., Craig, C. J., Pozo, A. D., Ladds, G., Hébert, T. E., Dodel, S., Prentice, H., Bejanyan, R., & Maudsley, S. (2026) · Frontiers in Science · doi:10.3389/fsci.2026.1891819 · Cited by 2 · see citing works
A Brief Review on Quantum Drug Design
Ray, A., Das, P., Bhattacharyya, S., Platoš, J., Mršić, L., & Hassanien, A. E. (2024) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-3980-6_41 · Cited by 1 · see citing works
Sheng, Z., Li, W., & Shuai, Z. (2025) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2024-1150 · Cited by 1 · see citing works
Automatic Molecule Fragmentation for Density Matrix Embedding Theory
Imamura, S., Iijima, N., Kasagi, A., & Yoshida, E. (2025) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.5c06027 · Cited by 1 · see citing works
Challenges and opportunities of quantum-computational chemistry
Li, Z., Liu, J., Shen, X., & Gao, F. (2022) · Scientia Sinica Chimica · doi:10.1360/ssc-2022-0222 · Cited by 1 · see citing works
Selected Configuration Interaction Using Time-Evolved Population Statistics
Weaving, T., Mingare, A., Ralli, A., & Coveney, P. V. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c01994 · Cited by 1 · see citing works
Additional file 1 of Modelling carbon capture on metal-organic frameworks with quantum computing
Greene‐Diniz, G., Manrique, D. Z., Sennane, W., Magnin, Y., Shishenina, E., Cordier, P., Llewellyn, P. L., Krompiec, M., Rančić, M. J., & Ramo, D. M. (2024) · Figshare · doi:10.6084/m9.figshare.26554478
Self-citationPerrier, E., & Aboy, M. (2026) · Perspectives in law, business and innovation · doi:10.1007/978-981-92-3768-5_3
Drug Discovery with Quantum Machine Learning
Pyrkov, A., Aliper, A., Bezrukov, D. S., & Zhavoronkov, A. A. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_10
Kelemen, Á. A., Perczel, A., & Jákli, I. (2026) · International Journal of Quantum Chemistry · doi:10.1002/qua.70160
Security and Efficiency of Delegated Quantum Computing
Leichtle, D. (2024) · theses.fr (ABES) · doi:10.70675/8a5d99d6z5531z465azb548z08e329a929c0
Designing Biologics with Quantum AI
Khan, M. Z. I., Sojib, N., Karim, M., & Roosan, D. (2026) · Springer eBooks · doi:10.1007/978-3-032-19536-4_6
Small Molecule Drug Discovery Enhanced by Quantum AI
Karim, M., Sojib, N., Khan, M. Z. I., & Roosan, D. (2026) · Springer eBooks · doi:10.1007/978-3-032-19536-4_8
Kitajima, H., Bistafa, C., Kobayashi, T., Kanno, S., Okada, O., Murasaki, K., Koyama, J., Saito, R., & Gao, Q. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00544
Quantum-Assisted Drug Discovery for Lung Cancer
Thopate, K., Naik, N., Patil, A., Pawar, A., & Pokale, T. (2026) · International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT) · doi:10.1109/tqcebt67648.2026.11681175
- Authors
- Kirsopp, J. J. M., Di Paola, C., Manrique, D. Z., Krompiec, M., Greene‐Diniz, G., Guba, W., Meyder, A., Wolf, D., Strahm, M., & Muñoz Ramo, D.
- Affiliations
- Cambridge Quantum Ltd Cambridge UK
- F. Hoffmann-La Roche AG (CH)
- Corresponding author
- Kirsopp, Josh J. M.
- ORCID
- Venue
- International Journal of Quantum Chemistry
- Publisher
- Wiley
- Published
- 2022-08-22
- Volume
- 122
- Issue
- 22
- ISSN
- 0020-7608 (ISSN-L)
- 1097-461X
- OpenAlex
- W3207824917
- Cited by
- 59
- References
- 68
- Citations by year
- 2026: 13 · 2025: 7 · 2024: 13 · 2023: 16 · 2022: 9
- FWCI
- 3.70
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2024-01-04)
- Open access
- green
- arxiv.org
- Locations
- International Journal of Quantum Chemistry (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- protein-ligand interactions, binding energy, density matrix embedding theory, variational quantum eigensolver, hybrid quantum-classical computation, NISQ devices, BACE1 inhibitors, transmon qubits, trapped-ion qubits, electronic ground state
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
