Quantum Computing in Molecular Design and Drug Discovery: A Systematic Literature Review
DOI:
https://doi.org/10.63158/journalisi.v8i1.1380Keywords:
quantum computing, molecular design, drug discovery, quantum algorithms, hybrid quantum-classical, NISQAbstract
This study examines how quantum computing (QC) is being applied to molecular design and drug discovery. This study aims to investigates how QC surpasses classical limitations, focusing on empirical performance in precision, accuracy, and optimisation tasks. Study design use PRISMA 2009 guidelines, 15 empirical studies (2020-2025) were included. Data were extracted on the drug-discovery stage, the algorithm used, evaluation metrics, benefits, and limitations. The findings show QC outperforms classical methods particularly through hybrid quantum–classical models. Thirteen studies reported superior gains, including AUC–ROC values of 0.80–0.95, +30% improvement in drug-likeness (QED), +6% increase in prediction accuracy, and up to 99% accuracy in drug–target interaction tasks. However, noisy intermediate-scale quantum (NISQ) hardware limitations and poor scalability limit real-world deployment, due to noise, and limited qubit counts. Consequently, current performance results are largely simulation-based rather than hardware-validated. In contrast to prior algorithm-centric reviews, this study provides a consolidated empirical synthesis and proposes a hybrid quantum–classical pipeline that maps high-performing algorithms across the drug discovery workflow under NISQ-era constraints. These findings inform pharmaceutical research and development by identifying realistic adoption pathways and the boundaries of current technological readiness.
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References
[1] C. Hasselgren and T. I. Oprea, “Artificial Intelligence for Drug Discovery: Are We There Yet?,” Annu. Rev. Pharmacol. Toxicol., vol. 64, no. September 2023, pp. 527–550, 2024, doi: 10.1146/annurev-pharmtox-040323-040828.
[2] D. P. Patil, R. D. Patil, and D. S. Kulkarni, “Quantum Computing in Drug Discovery: A Review of Foundations and Emerging Applications,” Int. J. Adv. Pharm. Sci. Res., vol. 5, no. 4, pp. 31–37, 2025, doi: 10.54105/ijapsr.e4078.05040625.
[3] Deepa Priya.V, J. Gnana Jersha, G. Madhubhavani, T. Mangalya, N. Balaharish Alias Yogesh, and T.R. Chellapandi, “Quantum Computing in Drug Discovery,” Int. Res. J. Adv. Eng. Manag., vol. 3, no. 03, pp. 770–778, Mar. 2025, doi: 10.47392/irjaem.2025.0123.
[4] Danishuddin, M. A. Haque, V. Kumar, S. N. Khan, and J.-J. Kim, “Quantum intelligence in drug discovery: Advancing insights with quantum machine learning.,” Drug Discov. Today, vol. 30, no. 10, p. 104463, Sep. 2025, doi: 10.1016/j.drudis.2025.104463.
[5] Y. Wang et al., “End-to-end differentiable construction of molecular mechanics force fields††Electronic supplementary information (ESI),” Chem. Sci., vol. 13, no. 41, pp. 12016–12033, 2022, doi: 10.1039/d2sc02739a.
[6] P. K. Prateek, N. K. Ojha, F. Altaf, and S. Maity, "Quantum secured 6G technology-based applications in Internet of Everything," Telecommun. Syst., vol. 82, no. 2, pp. 315-344, 2023, doi: 10.1007/s11235-022-00979-y.
[7] Khaled Fahd Al-Rashidi, “Quantum Computing in Pharmacology: Solving Complex Molecular Interactions,” Power Syst. Technol., vol. 48, no. 4, pp. 3690–3704, 2024, doi: 10.52783/pst.1218.
[8] D. Singh, “Quantum Computing Assays: Advancing Drug Metabolism Studies and Drug Delivery Design.,” 2024, United Arab Emirates. doi: 10.2174/0118723128358210250219103853.
[9] Kartheek Sankranthi, “Quantum Computing’s Future Role in AI-Driven Drug Discovery,” World J. Adv. Res. Rev., vol. 26, no. 2, pp. 1450–1458, 2025, doi: 10.30574/wjarr.2025.26.2.1692.
[10] X. Lin, X. Li, and X. Lin, “A review on applications of computational methods in drug screening and design,” Molecules, vol. 25, no. 6, pp. 1–17, 2020, doi: 10.3390/molecules25061375.
[11] P. Vareta, H. Muzenda, T. Nyamupaguma, Y. Dube, and B. Ndlovu, “The Rise of Quantum Computing and Its Impact on Cybersecurity,” Indones. J. Comput. Sci., vol. 14, no. 6, 2025, doi: 10.33022/ijcs.v14i6.5040.
[12] Islam, S.M.N., Kinger, P., Fatima, N., Kumar, A. (2024). Quantum Machine Learning: Bridging Classical and Quantum Frontiers. In: Alam, M.A., Siddiqui, F., Zafar, S., Hussain, I. (eds) Proceedings of 4th International Conference on ICT for Digital, Smart, and Sustainable Development. ICIDSSD 2024. Innovations in Sustainable Technologies and Computing. Springer, Singapore. doi:10.1007/978-981-97-7831-7_21
[13] R. Srivastava, “Quantum computing in drug discovery,” Inf. Syst. Smart City, vol. 1, no. 1, Dec. 2023, doi: 10.59400/issc.v1i1.294.
[14] J. Preskill, “Quantum Computing in the NISQ era and beyond,” Quantum, vol. 2, Jul. 2018, doi: 10.22331/q-2018-08-06-79.
[15] P. Maitireni, V. Ncube, B. Ndlovu, and T. Sibanda, “Quantum Computing Cryptography : A Systematic Review of Innovations , Applications , Challenges , and Algorithms,” vol. 7, no. 4, pp. 3668–3710, 2025, doi: 10.63158/journalisi.v7i4.1331
[16] J. C. L. Chow, “Quantum Computing in Medicine.,” Med. Sci. (Basel, Switzerland), vol. 12, no. 4, Nov. 2024, doi: 10.3390/medsci12040067.
[17] V. S. Gomase, A. P. Ghatule, R. Sharma, and S. P. Dhamane, “Quantum Computing in Drug Discovery Techniques, Challenges, and Emerging Opportunities.,” Curr. Drug Discov. Technol., Aug. 2025, doi: 10.2174/0115701638371707250729040426.
[18] A. Liberati et al., “The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration,” BMJ, vol. 339, p. b2700, 2009, doi: 10.1136/bmj.b2700.
[19] D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement,” BMJ, vol. 339, no. 7716, pp. 332–336, 2009, doi: 10.1136/bmj.b2535.
[20] G. Pallavi, A. Altalbe, and R. P. Kumar, “QKDTI A quantum kernel based machine learning model for drug target interaction prediction.,” Sci. Rep., vol. 15, no. 1, p. 27103, 2025, doi: 10.1038/s41598-025-07303-z.
[21] Y. S, P. Naik, D. B R, C. Patil, and P. Goudanavar, “Quantum-inspired computational drug design for phytopharmaceuticals: a herbal holography analysis.,” J. Mol. Model., vol. 31, no. 7, p. 188, Jun. 2025, doi: 10.1007/s00894-025-06412-w.
[22] A. Haverly and S. Rahimi, “Protein Folding and Drug Discovery Using Quantum Computing’s Grover’s Algorithm,” in 2025 International Conference on Quantum Communications, Networking, and Computing (QCNC), 2025, pp. 427–431. doi: 10.1109/QCNC64685.2025.00073.
[23] W. Li, Z. Yin, X. Li, D. Ma, S. Yi, and Z. Zhang, “A Quantum Computing Pipeline for Real World Drug Discovery : From Algorithm to Quantum Hardware,” Sci. Rep., vol. 14, no. 16942, 2024.
[24] Y. Li et al., “Efficient molecular conformation generation with quantum-inspired algorithm.,” J. Mol. Model., vol. 30, no. 7, p. 228, Jun. 2024, doi: 10.1007/s00894-024-05962-9.
[25] T. Bikku, K. K. Malligunta, S. Thota, and P. P. Surapaneni, "Improved quantum algorithm: a crucial stepping stone in quantum-powered drug discovery," J. Electron. Mater., vol. 54, no. 5, pp. 3434-3443, 2025, doi: 10.1007/s11664-024-11275-7.
[26] P.-Y. Kao et al., “Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry.,” J. Chem. Inf. Model., vol. 63, no. 11, pp. 3307–3318, Jun. 2023, doi: 10.1021/acs.jcim.3c00562.
[27] L. Domingo et al., “A Hybrid Quantum-classical Fusion Neural Network to Improve Protein-ligand Binding Affinity Predictions for Drug Discovery,” in 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 2024, pp. 126–131. doi: 10.1109/QCE60285.2024.10265.
[28] M. Anoshin et al., “Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation,” IEEE Trans. Quantum Eng., vol. 5, pp. 1–14, 2024, doi: 10.1109/TQE.2024.3414264.
[29] A. S. Bhatia, M. K. Saggi, and S. Kais, “Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery.,” J. Chem. Inf. Model., vol. 63, no. 21, pp. 6476–6486, Nov. 2023, doi: 10.1021/acs.jcim.3c01079.
[30] S. Surya Prakash, P. Banu Priya, S. Someneni, Udendhran, and A. Banerjee, “Drug Discovery Using Variational Quantum EigenSolver BT - Proceedings of Fourth International Conference on Computing and Communication Networks,” A. Kumar, A. Swaroop, and P. Shukla, Eds., Singapore: Springer Nature Singapore, 2025, pp. 691–703.
[31] A. Ajagekar and F. You, “Molecular design with automated quantum computing-based deep learning and optimization,” 2023, doi: 10.1038/s41524-023-01099-0.
[32] H. Mustafa, S. N. Morapakula, P. Jain, and S. Ganguly, “Variational Quantum Algorithms for Chemical Simulation and Drug Discovery,” 2022 Int. Conf. Trends Quantum Comput. Emerg. Bus. Technol. TQCEBT 2022, no. October, 2022, doi: 10.1109/TQCEBT54229.2022.10041453.
[33] J. Li, M. Alam, C. M. Sha, J. Wang, N. V Dokholyan, and S. Ghosh, “Invited: Drug Discovery Approaches Using Quantum Machine Learning,” in Proceedings of the 58th Annual ACM/IEEE Design Automation Conference, IEEE Press, 2022, pp. 1356–1359. doi: 10.1109/DAC18074.2021.9586268.
[34] W. M. Kirby, A. Tranter, and P. J. Love, “Contextual Subspace Variational Quantum Eigensolver,” Quantum, vol. 5, no. 456, p. 456, 2021.
[35] D. A. Fedorov, B. Peng, N. Govind, and Y. Alexeev, “Open Access VQE method : a short survey and recent developments,” Mater. Theory, vol. 6, no. Article number 2, pp. 1–21, 2022, doi: 10.1186/s41313-021-00032-6.
[36] J. Tilly et al., “The Variational Quantum Eigensolver: A review of methods and best practices,” Phys. Rep., vol. 986, pp. 1–128, 2022, doi: 10.1016/j.physrep.2022.08.003.
[37] M. Akrom, “Quantum Support Vector Machine for Classification Task : A Review,” J. Multiscale Mater. Informatics, vol. 1, no. 2, pp. 1–8, 2024, doi: 10.62411/jimat.v1i2.10965.
[38] M. Avramouli, I. Savvas, A. Vasilaki, G. Garani, and A. Xenakis, “Quantum Machine Learning in Drug Discovery: Current State and Challenges,” in Proceedings of the 26th Pan-Hellenic Conference on Informatics, in PCI ’22. New York, NY, USA: Association for Computing Machinery, 2023, pp. 394–401. doi: 10.1145/3575879.3576024.
[39] M. A. Hafeez and A. Munir, “H-QNN : A Hybrid Quantum – Classical Neural Network for Improved Binary Image Classification,” pp. 1462–1481, 2024.
[40] O. J. Samuel, “Quantum Neural Networks for Accelerating Drug Discovery in Regenerative Medicine,” World J. Adv. Eng. Technol. Sci., pp. 1–13, 2024.
[41] J. Li, R. O. Topaloglu, and S. Ghosh, “Quantum Generative Models for Small Molecule Drug Discovery,” IEEE Trans. Quantum Eng., vol. 2, 2021, doi: 10.1109/TQE.2021.3104804.
[42] K. Zhang and V. E. Korepin, “Depth optimization of quantum search algorithms beyond Grover’s algorithm,” Phys. Rev. A, vol. 101, no. 3, pp. 1–13, 2020, doi: 10.1103/PhysRevA.101.032346.
[43] M. AbuGhanem and H. Eleuch, “NISQ Computers: A Path to Quantum Supremacy,” IEEE Access, vol. 12, pp. 102941–102961, 2024, doi: 10.1109/ACCESS.2024.3432330.
[44] M. Joshi, M. K. Mishra, and S. Karthikeyan, “Impact of hardware connectivity on Grover’s algorithm in NISQ era,” Quantum Inf. Process., vol. 24, no. 4, p. 118, 2025, doi: 10.1007/s11128-025-04733-6.
[45] Y. Wang and P. S. Krstic, “Prospect of using Grover’s search in the noisy-intermediate-scale quantum-computer era,” Phys. Rev. A, vol. 102, no. 4, p. 042609, 2020, doi: 10.1103/PhysRevA.102.042609.
[46] A. Kovyrshin, L. Tornberg, J. Crain, S. Mensa, I. Tavernelli, and A. Broo, “Prioritizing quantum computing use cases in the drug discovery and development pipeline.,” Drug Discov. Today, vol. 30, no. 3, p. 104323, Mar. 2025, doi: 10.1016/j.drudis.2025.104323.
[47] M. Cerezo et al., “Variational quantum algorithms,” Nat. Rev. Phys., vol. 3, no. 9, pp. 625–644, 2021, doi: 10.1038/s42254-021-00348-9.
[48] T. Kanao and H. Goto, “Simulated bifurcation assisted by thermal fluctuation,” Commun. Phys., vol. 5, no. 153, p. 153, 2022, doi: 10.1038/s42005-022-00929-9.
[49] B. Lau et al., “Insights from incorporating quantum computing into drug design workflows.,” Bioinformatics, vol. 39, no. 1, Jan. 2023, doi: 10.1093/bioinformatics/btac789.
[50] P. U. Kulkarni, H. Shah, and V. K. Vyas, “Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) Simulation: A Tool for Structure-Based Drug Design and Discovery.,” Mini Rev. Med. Chem., vol. 22, no. 8, pp. 1096–1107, 2022, doi: 10.2174/1389557521666211007115250.
[51] S. Y. Kim, S. J. Jeon, and H. Y. Kwon, “Smart City, Net Neutrality, and Antitrust: findings from Korea,” in Proceedings of the 21st Annual International Conference on Digital Government Research, in dg.o ’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 90–96. doi: 10.1145/3396956.3396990.
[52] Y. Li, X. Cui, Z. Xiong, Z. Zou, B. Liu, B. Y. Wang, ... and M. H. Yung, "Efficient molecular conformation generation with quantum-inspired algorithm," J. Mol. Model., vol. 30, no. 7, p. 228, 2024, doi: 10.1007/s00894-024-05962-9.
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