Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications

Blunt, Nick S.; Camps, Joan; Crawford, Ophelia; Izsák, Róbert; Leontica, Sebastian; Mirani, Arjun; Moylett, Alexandra E.; Scivier, Sam A.; Sünderhauf, Christoph; Schopf, Patrick; Taylor, Jacob M.; Holzmann, Nicole

doi:10.1021/acs.jctc.2c00574

Cited by 192Quantum Computing Algorithms and ArchitectureAdvanced Chemical Physics StudiesComputational Drug Discovery Methods

Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications

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Blunt, Nick S., Camps, Joan, Crawford, Ophelia, Izsák, Róbert, Leontica, Sebastian, Mirani, Arjun, Moylett, Alexandra E., Scivier, Sam A. +4 more·2022·Journal of Chemical Theory and Computation

DOI 10.1021/acs.jctc.2c00574PMC PMC9753588

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Abstract

Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation of molecules to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture that we describe. Our estimates showcase that recent developments on quantum phase estimation algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterization approach to just a few days with sparse qubitization, painting a picture of fast and exciting progress in this nascent field.

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SpinQ Technology developed the first desktop quantum computer with a high-speed processor.
“Recently, a startup company from China known as SpinQ Technology informed about the development of the first-desktop quantum computer with a high-speed processor [8, 19, 38, 39].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [39]
The Introduction discusses quantum hardware from Google, USTC and IBM, but does not mention SpinQ Technology. Searches for SpinQ, a desktop quantum computer and a high-speed processor found no passage in our copy addressing the claimed development. (LLM, conf=0.74)
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Background
Quantum computing in bioinformatics: a systematic review mapping
Nałęcz-Charkiewicz, K., Charkiewicz, K., & Nowak, R. M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae391 · Cited by 54 · see citing works
“[5] is a review of articles on drug discovery.”
Background
Block-encoding structured matrices for data input in quantum computing
Sünderhauf, C., Campbell, E. T., & Camps, J. (2024) · Quantum · doi:10.22331/q-2024-01-11-1226 · Cited by 50 · see citing works
“Not all gates are equal in error correction codes, rather, in the popular surface codes [24], so-called T gates and Toﬀoli gates (which can be reexpressed with T gates) are much more expensive than Cliﬀord gates [25–29].”
BackgroundSelf-citation
State of the Art in Parallel and Distributed Systems: Emerging Trends and Challenges
Dai, F., Hossain, M. A., & Wang, Y. (2025) · Electronics · doi:10.3390/electronics14040677 · Cited by 47 · see citing works
Molecular representations in bio-cheminformatics
Nguyen‐Vo, T., Teesdale‐Spittle, P. H., Harvey, J. E., & Nguyen, B. P. (2024) · Memetic Computing · doi:10.1007/s12293-024-00414-6 · Cited by 52 · see citing works
“The potential applications of quantum computing in drug discovery have been extensively discussed [119–121].”
Quantifying the effect of gate errors on variational quantum eigensolvers for quantum chemistry
Dalton, K., Long, C. K., Yordanov, Y. S., Smith, C. G., Barnes, C. H. W., Mertig, N., & Arvidsson-Shukur, D. R. M. (2024) · npj Quantum Information · doi:10.1038/s41534-024-00808-x · Cited by 43 · see citing works
“However, the QPEA requires executing millions of gates on error-corrected hardware 7 .”
Method
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
“SpinQ Technology from China has also ventured to make high-speed quantum chips [1, 32–34].”
A Vision for the Future of Multiscale Modeling
Capone, M., Romanelli, M., Castaldo, D., Parolin, G., Bello, A., Gil, G., & Vanzan, M. (2024) · ACS Physical Chemistry Au · doi:10.1021/acsphyschemau.3c00080 · Cited by 38 · see citing works
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
Quantum circuit optimization with AlphaTensor
Ruiz, F. J. R., Laakkonen, T., Bausch, J., Balog, M., Barekatain, M., Heras, F. J. H., Novikov, A., Fitzpatrick, N., Romera‐Paredes, B., van de Wetering, J., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01001-1 · Cited by 44 · see citing works
Quantum computers: what are they good for?
Brooks, M. (2023) · Nature · doi:10.1038/d41586-023-01692-9 · Cited by 36 · see citing works
Quantum computation for periodic solids in second quantization
Ivanov, A. V., Sünderhauf, C., Holzmann, N., Ellaby, T., Kerber, R. N., Jones, G. A., & Camps, J. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.013200 · Cited by 33 · see citing works
“While VQE might have its merits in certain use cases, it appears the emerging consensus is that QPE has a superior scaling with the system size [1, 55].”
BackgroundSelf-citation
Improved Quantum Algorithm: A Crucial Stepping Stone in Quantum-Powered Drug Discovery
Bikku, T., Malligunta, K. K., Thota, S., & Surapaneni, P. P. (2024) · Journal of Electronic Materials · doi:10.1007/s11664-024-11275-7 · Cited by 28 · see citing works
“15 Zinner et al. reported that quantum algorithms can be used on machines to study pharmaceutical designs.”
From GPUs to AI and quantum: three waves of acceleration in bioinformatics
Schmidt, B., & Hildebrandt, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103990 · Cited by 28 · see citing works
Statistical Phase Estimation and Error Mitigation on a Superconducting Quantum Processor
Blunt, N. S., Caune, L., Izsák, R., Campbell, E. T., & Holzmann, N. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.040341 · Cited by 27 · see citing works
Self-citation
Exploring the future of biopharmaceutical drug discovery: can advanced AI platforms overcome current challenges?
Bettanti, A., Beccari, A. R., & Biccarino, M. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00188-3 · Cited by 21 · see citing works
“The advent of quantum computing is poised to revolutionize AIDD, offering the potential to significantly enhance the speed, accuracy, and scope of pharmaceutics [134, 135].”
Nature meets machine: the AI renaissance in natural product drug discovery
Muthuraj, R., & Chandrasekaran, J. (2026) · Natural Products and Bioprospecting · doi:10.1007/s13659-025-00589-6 · Cited by 24 · see citing works
“This same cost barrier will likely apply to early implementations of quantum-enhanced methods until more efficient hybrid solutions are developed [175].”
Modeling Heterogeneous Catalysis Using Quantum Computers: An Academic and Industry Perspective
Hariharan, S., Kinge, S., & Visscher, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01212 · Cited by 20 · see citing works
Quantum simulations of chemistry in first quantization with any basis set
Georges, T. N., Bothe, M., Sünderhauf, C., Berntson, B. K., Izsák, R., & Ivanov, A. V. (2025) · npj Quantum Information · doi:10.1038/s41534-025-00987-1 · Cited by 21 · see citing works
“Speci ﬁ cally,QPEwith qubitization is the leading approach 14 – 16 whichrequires the lowest quantum resources for quantum chemistry problems 2,8,17 – 19 .”
Self-citation
How generative Artificial Intelligence can transform drug discovery?
Jusoh, A. S., Remli, M. A., Mohamad, M. S., Cazenave, T., & Fong, C. (2025) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2025.117825 · Cited by 21 · see citing works
“Furthermore, the exploration of novel generative architectures, particularly diffusion models [146,147], could revolutionize the field of Generative AI for drug discovery.”
Atomic Quantum Technologies for Quantum Matter and Fundamental Physics Applications
Malo, J. Y., Lepori, L., Gentini, L., & Chiofalo, M. L. (2024) · Technologies · doi:10.3390/technologies12050064 · Cited by 20 · see citing works
Eliminating Imaginary Vibrational Frequencies in Quantum-Chemical Cluster Models of Enzymatic Active Sites
Bowling, P. E., Dasgupta, S., & Herbert, J. M. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00221 · Cited by 18 · see citing works
Fusion of quantum computing and explainable AI: A comprehensive survey on transformative healthcare solutions
Singh, S. S., Kumar, S., Ahuja, R., & Barua, J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103217 · Cited by 20 · see citing works
Computational strategies for antimicrobial discovery: From machine learning to multiscale simulation
Wu, Y. J., Chen, Z., Chen, X., Li, C., Zengin, G., & Li, M. (2025) · Current Molecular Pharmacology · doi:10.1016/j.cmp.2025.10.003 · Cited by 17 · see citing works
Towards Building Quantum Resistant Blockchain
Chauhan, S., Ojha, V. P., Yarahmadian, S., & Carvalho, D. B. (2023) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet58911.2023.10389558 · Cited by 16 · see citing works
“The introduction of Shor’s algorithm [8], which can factorize primes in O ( √ N ) time compared to classical computers’ O ( N ) time [5, 8], has raised concerns about the vulnerability of classical cryptography, particularly in key generation methods.”
Background
Quantum intelligence in drug discovery: Advancing insights with quantum machine learning
Danishuddin, Haque, M. A., Kumar, V., Khan, S. N., & Kim, J.-J. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104463 · Cited by 15 · see citing works
Quantum Molecular Docking with a Quantum-Inspired Algorithm
Li, Y., Cui, X., Xiong, Z., Liu, B., Wang, B.-Y., Shu, R., Qiao, N., & Yung, M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00141 · Cited by 16 · see citing works
Recent advances in computational antimicrobial peptide discovery through big data, modeling, and artificial intelligence and their interplay in ushering the next golden era of drug development
Ibisanmi, T., Jiang, X., Willcox, M. D. P., & Kumar, N. (2026) · Frontiers Bioinform. · doi:10.3389/fbinf.2026.1749404 · Cited by 14 · see citing works
“Quantum computing, for example, is emerging as a possible adjunct to speed up the discovery process (Li et al., 2024; Kumar et al., 2024; Blunt et al., 2022).”
Reducing Entanglement with Physically Inspired Fermion-To-Qubit Mappings
Parella-Dilmé, T., Kottmann, K., Zambrano, L., Mortimer, L., Kottmann, J. S., & Acín, A. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.030333 · Cited by 14 · see citing works
“Electronic structure ab-initio quantum chemistry simulations play a pivotal role in various fields, including drug development [1, 2], computational catalysis [3], and material science [4].”
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 this setting, quantum subroutines can capture subtle electronic changes that classical fixed-charge force fields must approximate, with proof-of-concept studies reported for catalytic motifs in viral proteases and metalloenzymes [66, 67].”
Influential
Quantum simulation of preferred tautomeric state prediction
Shee, Y., Yeh, T.-L., Hsiao, J.-Y., Yang, A., Lin, Y., & Hsieh, M.-H. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00767-9 · Cited by 13 · see citing works
Compilation of a simple chemistry application to quantum error correction primitives
Blunt, N. S., Gehér, G. P., & Moylett, A. E. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013325 · Cited by 11 · see citing works
“These physical error rates are commonly used when estimating the resource requirements of fault-tolerant quantum algorithms [4, 12, 19].”
BackgroundSelf-citation
Accurate quantum-centric simulations of intermolecular interactions
Kaliakin, D. S., Shajan, A., Liang, F., Moreno, J. R., Li, Z., Mitra, A., Motta, M., Johnson, C., Saki, A. A., Das, S., et al. (2025) · Communications Physics · doi:10.1038/s42005-025-02305-9 · Cited by 11 · see citing works
“Further examplesofsimulationsthatcouldbeenabledbyourapproachinclude quantumcomputingsimulationsofchemicalreactivityof CO 2 - ﬁ xating ruthenium catalyst proposed by Burg et al. 83 , Ibrutinib drug simulations proposed by Blunt et al. 84 , and the drug-discovery work ﬂ ows proposed by Pyrkov et al. 85 and Kumar et al.…”
What is the plausibility that all drugs will be designed by computers by the end of the decade?
Medina‐Franco, J. L., & López‐López, E. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2331734 · Cited by 11 · see citing works
“Emerging technologies such as quantum computing [14,15] are anticipated to continue playing a pivotal role in selecting and identifying hits, their development, and rational drug repurposing.”
Background
ReAIM: A ReRAM-based Adaptive Ising Machine for Solving Combinatorial Optimization Problems
Chiang, H.-W., Nien, C.-F., Cheng, H.-Y., & Huang, K.-P. (2024) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca59077.2024.00015 · Cited by 10 · see citing works
“The Ising machine (IM) is a quantum-inspired system with promising capabilities in solving 1 combinatorial optimization problems (COPs) [1], [37], [40], [50], widely applied in ﬁnance [47], drug development [8], and logistics [42].”
Background
SHARC meets TEQUILA: mixed quantum-classical dynamics on a quantum computer using a hybrid quantum-classical algorithm
Gil, E. S., Oppel, M., Kottmann, J. S., & González, L. (2024) · Chemical Science · doi:10.1039/d4sc04987j · Cited by 11 · see citing works
Workflow for practical quantum chemical calculations with a quantum phase estimation algorithm: electronic ground and π–π* excited states of benzene and its derivatives
Ino, Y., Yonekawa, M., Yuzawa, H., Minato, Y., & Sugisaki, K. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp03454f · Cited by 11 · see citing works
“13 Considering such rapid progress in quantum hardware, the research of the FTQC algorithms is an urgent issue.”
Development of Quantum Protocol Modification CSLOE–2022, Increasing the Cryptographic Strength of Classical Quantum Protocol BB84
Cherckesova, L. V., Safaryan, O. A., Beskopylny, A. N., & Revyakina, E. A. (2022) · Electronics · doi:10.3390/electronics11233954 · Cited by 10 · see citing works
Position Papers for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking
Lougovski, P., Parekh, O., Broz, J., Byrd, M., Chembo, Y. K., de Jong, W. A., Figueroa, E. V., Humble, T. S., Larson, J. M., Quiroz, G., et al. (2023) · doi:10.2172/2283320 · Cited by 10 · see citing works
Hückel molecular orbital theory on a quantum computer: A scalable system-agnostic variational implementation with compact encoding
Singh, H., Majumder, S., & Mishra, S. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0210597 · Cited by 9 · see citing works
“In contrast, quantum computers operating with principles of quantum mechanics are particularly well-suited for simulating molecular quantum phenomena in the fields of catalysis, materials research, and drug discovery [15–17].”
Background
Mitigating Errors on Superconducting Quantum Processors Through Fuzzy Clustering
Ahmad, H. G., Schiattarella, R., Mastrovito, P., Chiatto, A., Levochkina, A., Esposito, M., Montemurro, D., Pepe, G. P., Bruno, A., Tafuri, F., et al. (2024) · Advanced Quantum Technologies · doi:10.1002/qute.202300400 · Cited by 9 · see citing works
Metrics for spin-based computing
Kurebayashi, H., Finocchio, G., Everschor‐Sitte, K., Gartside, J. C., Taniguchi, T., Litvinenko, A. N., Kumar, A., Åkerman, J. J., Vasilaki, E., Selçuk, K., et al. (2026) · Nature Reviews Physics · doi:10.1038/s42254-025-00918-1 · Cited by 13 · see citing works
“Common COPs include resource allocation 138 , scheduling 139 , graph partitioning 140,141 , drug design 142,143 , routing in logistics 144,145 , designing efficient networks 146 , and even enhancing machine learning and artificial intelligence models 147,148 .”
A fault-tolerant variational quantum algorithm with limited T-depth
Sayginel, H., Jamet, F., Agarwal, A., Browne, D. E., & Rungger, I. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/ad0571 · Cited by 9 · see citing works
A Review on Smart Healthcare Employing Quantum Internet of Things
Sutradhar, K., Venkatesh, R., & Venkatesh, P. (2024) · IEEE Engineering Management Review · doi:10.1109/emr.2024.3440835 · Cited by 9 · see citing works
Unveiling Advanced Computational Applications in Quantum Computing: A Comprehensive Review
Markna, J. H., Palatia, T. P., Gohel, S., & Kataria, B. R. (2023) · International Journal of Advanced Nano Computing and Analytics · doi:10.61797/ijanca.v2i2.284 · Cited by 8 · see citing works
Quantum Internet of Things for Smart Healthcare
Sutradhar, K., Venkatesh, R., & Venkatesh, P. (2023) · Springer eBooks · doi:10.1007/978-3-031-50514-0_12 · Cited by 8 · see citing works
Visioning synthetic futures for yeast research within the context of current global techno-political trends
Dixon, T. A., Walker, R. S. K., & Pretorius, I. S. (2023) · Yeast · doi:10.1002/yea.3897 · Cited by 8 · see citing works
Navigating advanced technology transitions: using lessons from nanotechnology
Maynard, A., & Dudley, S. M. (2023) · Nature Nanotechnology · doi:10.1038/s41565-023-01481-5 · Cited by 8 · see citing works
Challenges and opportunities in quantum computing in healthcare
Sundaram, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29297-2.00010-1 · Cited by 7 · see citing works
SHARC-VQE: Simplified Hamiltonian approach with refinement and correction enabled variational quantum eigensolver for molecular simulation
Singh, H., Majumder, S., & Mishra, S. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0249447 · Cited by 7 · see citing works
“Quantum computers are particularly suitable for simulating structure and properties in the areas of catalysis, materials research, and drug discovery [15–17].”
Background
Quantum Computation of Electronic Structure with Projector Augmented-Wave Method and Plane Wave Basis Set
Ivanov, A. V., Patterson, A. D., Bothe, M., Sünderhauf, C., Berntson, B. K., Mortensen, J. J., Kuisma, M. J., Campbell, E. T., & Izsák, R. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00551 · Cited by 8 · see citing works
Self-citation
Quantum Computing in Drug Discovery
Muniasamy, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2043-4.ch003 · Cited by 7 · see citing works
Molecular Symmetry in VQE: A Dual Approach for Trapped-Ion Simulations of Benzene
Goings, J. J., Zhao, L., Jakowski, J., Morris, T., & Pooser, R. C. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.10187 · Cited by 7 · see citing works
A brief review on quantum computing based drug design
Das, P., Ray, A., Bhattacharyya, S., Platoš, J., Snåšel, V., Mršić, L., Huang, T., & Zelinka, I. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1553 · Cited by 8 · see citing works
“Although quantum computing has made computational chemistry (a tool for drug design) more advanced and faster (Blunt et al., 2022), the applications of quantum computing in drug design and discovery are still at a very nascent research stage.”
Enhancing the electron pair approximation with measurements on trapped-ion quantum computers
Zhao, L., Wang, Q., Goings, J. J., Shin, K., Kyoung, W., Noh, S., Rhee, Y. M., & Kim, K. (2024) · npj Quantum Information · doi:10.1038/s41534-024-00871-4 · Cited by 6 · see citing works
“Published in partnership with The University of New South Wales https://doi.org/10.1038/s41534-024-00871-4 Enhancing the electron pair approximation with measurements on trapped-ion quantum computers”
Influential
Ab initio extended Hubbard model of short polyenes for efficient quantum computing
Yoshida, Y., Takemori, N., & Mizukami, W. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0213525 · Cited by 6 · see citing works
QUASIM: Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing
Maaß, W., Agrawal, A., Ciani, A., Danz, S., Delgadillo, A., Ganser, P., Kienast, P., Kulig, M., König, V., Rodellas-Gràcia, N., et al. (2024) · Künstliche Intell. · doi:10.1007/s13218-024-00860-x · Cited by 6 · see citing works
“Quantum computing technologies are evaluated in various domains, such as finance [27], production [7], chemical modeling [52], and drug design [15].”
Background
Evaluating Variational Quantum Eigensolver Approaches for Simplified Models of Molecular Systems: A Case Study on Protocatechuic Acid
de Jesus, G. F., Teixeira, E. S., Galvão, L. Q., da Silva, M. H. F., Neto, M. Q. N., Fernandez, B. O., de Lima, A. M., Silva, E. D., & Cruz, C. (2024) · Molecules · doi:10.3390/molecules30010119 · Cited by 6 · see citing works
Quantum Computing and Its Promise in Drug Discovery
Mishra, R., Mishra, P. S., Mazumder, R., Mazumder, A., & Varshney, S. (2024) · doi:10.1002/9781394159338.ch3 · Cited by 6 · see citing works
Scalable narrow linewidth high power laser for barium ion optical qubits
Ahmadi, M., Dutta, T., & Mukherjee, M. (2024) · Optics Express · doi:10.1364/oe.520371 · Cited by 5 · see citing works
Distributed Quantum Error Correction Based on Hyperbolic Floquet Codes
Sutcliffe, E., Jonnadula, B., Gall, C. L., Moylett, A. E., & Westoby, C. M. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00076 · Cited by 8 · see citing works
“These codes enable reliable operations in the presence of noise, at the cost of requiring thousands or even millions of physical qubits for industrially relevant applications [3]–[7].”
Self-citation
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
Modern Drug Research and Its Impact on Pharmaceutical Industries
Nath, R., Kityania, S., Das, S. K., Nath, D., Patra, J. K., & Talukdar, A. D. (2024) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-97-4600-2_17 · Cited by 4 · see citing works
Research and Application of Quantum Computing Algorithm for Asset Portfolio Optimization
Wang, J., & Tian, B. (2024) · International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI) · doi:10.1109/icdacai65086.2024.00042 · Cited by 4 · see citing works
The Electronic Structure of the Hydrogen Molecule: A Tutorial Exercise in Classical and Quantum Computation
Graves, V., Sünderhauf, C., Blunt, N. S., Izsák, R., & Szöri, M. (2025) · ACS Physical Chemistry Au · doi:10.1021/acsphyschemau.5c00030 · Cited by 6 · see citing works
Self-citation
Unfolding Post-Quantum Cryptosystems: CRYSTALS-Dilithium, McEliece, BIKE, and HQC
Ojha, V. P., Chauhan, S., Yarahmadian, S., & Carvalho, D. B. (2025) · Mathematics · doi:10.3390/math13172841 · Cited by 4 · see citing works
Quantum computing applications in biology
Ghamsari, M. S. (2025) · Discover Computing · doi:10.1007/s10791-025-09803-y · Cited by 4 · see citing works
Quantum Computer Simulation of Protein Protonation
Hu, H. Q. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00606 · Cited by 4 · see citing works
Solvent Distribution Effects on Quantum Chemical Calculations with Quantum Computers
Yoshida, Y., Mizukami, W., & Yoshida, N. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01189 · Cited by 5 · see citing works
Quantum computing in Healthcare 5.0
Ashour, A. S., & Koundal, D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29297-2.00009-5 · Cited by 5 · see citing works
Unified framework for matchgate classical shadows
Heyraud, V., Chomet, H., & Tilly, J. (2025) · npj Quantum Information · doi:10.1038/s41534-025-01015-y · Cited by 4 · see citing works
“Understanding the physics of correlated electronic systems is crucial for quantum chemistry [1–3] and condensed matter physics [4, 5], with potential far-reaching applications in drug discovery [6], chemical engineering [7] and material science [8].”
Molecular insights into the sensing function of an oxidized graphene flake for the adsorption of Avigan antiviral drug
Pecho, R. D. C., Hajali, N., Silguera, R. D. T., Qassem, L. Y., Alwan, M., Jawad, M. J., Cayllahua, F. C., Mirzaei, M., & Akhavan‐Sigari, R. (2023) · Computational and Theoretical Chemistry · doi:10.1016/j.comptc.2023.114240 · Cited by 4 · see citing works
The Second Quantum Revolution: Unexplored Facts and Latest News
Intonti, K., Viscardi, L., Lamberti, V., Matteucci, A., Micciola, B., Modestino, M. R., & Noce, C. (2024) · Encyclopedia · doi:10.3390/encyclopedia4020040 · Cited by 8 · see citing works
“The ability to model atomic interactions with unprecedented precision accelerates the development of new materials and drugs, thus reducing the time and costs associated with research and development [104].”
Background
Quantum Computational Concepts and Approaches in Drug Discovery, Development and Delivery
Meenakshi, D. U., Kesavan, S. M., Francis, A. P., & Khan, S. A. (2024) · doi:10.1002/9781394159338.ch1 · Cited by 4 · see citing works
Challenges and Advances in the Simulation of Targeted Covalent Inhibitors Using Quantum Computing
Chaudhuri, S., Huynh, B. C., Amory, R., Rogers, D. M., Cranney, A., Martínez-Martínez, L. Á., Macrì, T., Jones, G., Sheridan, E., Khedkar, A., et al. (2025) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5c01680 · Cited by 5 · see citing works
Application of artificial intelligence in the development of circular bioeconomy
Mishra, R., & Pandey, D. K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26779-6.00014-0 · Cited by 3 · see citing works
RobQFL: Robust Quantum Federated Learning in Adversarial Environment
Maouaki, W. E., Innan, N., Marchisio, A., Said, T., Shafique, M., & Bennai, M. (2025) · IEEE International Conference on Quantum Artificial Intelligence (QAI) · doi:10.1109/qai63978.2025.00027 · Cited by 4 · see citing works
“Proof-of-concepts already span diverse areas—from cancer diagnosis [10], [11] and drug discovery [12] to finance [13]–[23], and cybersecurity [24]—showing QML’s broad practical reach.”
Selectivity Mechanism of Pyrrolopyridone Analogues Targeting Bromodomain 2 of Bromodomain-Containing Protein 4 from Molecular Dynamics Simulations
Shi, M., Zheng, X., Zhou, Y., Yin, Y., Zhou, L., Zou, Z., Hu, Y., Liang, Y., Chen, T., Yang, Y., et al. (2023) · ACS Omega · doi:10.1021/acsomega.3c03935 · Cited by 3 · see citing works
Quantum Computing Approach to Fixed-Node Monte Carlo Using Classical Shadows
Blunt, N. S., Caune, L., & Quiroz-Fernandez, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01468 · Cited by 4 · see citing works
Self-citation
T-Gate Enabled Fault-Tolerant Ansatz Circuit Design for Variational Quantum Algorithms in Peptide Folding on Aria-1
Uttarkar, A., Setlur, A. S., & Niranjan, V. (2024) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/globalaisummit62156.2024.10947993 · Cited by 3 · see citing works
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Revolutionizing Drug Discovery: The Transformative Role of Quantum Computing in Pharmaceutical Research
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Authors: Blunt, N. S., Camps, J., Crawford, O., Izsák, R., Leontica, S., Mirani, A., Moylett, A. E., Scivier, S. A., Sünderhauf, C., Schopf, P., Taylor, J. M., & Holzmann, N.
Affiliations: Riverlane (United Kingdom) (GB)
Astex Pharmaceuticals, 436 Cambridge Science Park, Cambridge CB4 0QA, United Kingdom
Corresponding author: Holzmann, Nicole
ORCID: Blunt, Nick S.
Crawford, Ophelia
Izsák, Róbert
Leontica, Sebastian
Mirani, Arjun
Moylett, Alexandra E.
Scivier, Sam A.
Sünderhauf, Christoph
Taylor, Jacob M.
Holzmann, Nicole
Venue: Journal of Chemical Theory and Computation
Publisher: American Chemical Society (ACS)
Published: 2022-11-10
Volume: 18
Issue: 12
Pages: 7001-7023
ISSN: 1549-9618 (ISSN-L)
1549-9626
DOI: 10.1021/acs.jctc.2c00574
PMC: PMC9753588
OpenAlex: W4308683072
Cited by: 192
References: 166
Citations by year: 2026: 40 · 2025: 59 · 2024: 60 · 2023: 29 · 2022: 3
FWCI: 2.63
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: Journal of Chemical Theory and Computation (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Astex Pharmaceuticals
Innovate UK (105622)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords: quantum computing, drug discovery, quantum chemistry, quantum phase estimation, Trotterization, protein-drug complexes, ibrutinib, quantum algorithms
MeSH terms: Computing Methodologies, Pharmaceutical Preparations, Electrons, Quantum Theory, Drug Discovery

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