Quantum computing in drug discovery
Abstract
Quantum computers are recently being developed in wide varieties, but the computational results from quantum computing have been largely confined to constructing artificial assignments. The applications of quantum computers to real-world problems are still an active area of research. However, challenges arise when the limits of scale and complexity in biological problems are pushed, which has affected drug discovery. The fast-evolving quantum computing technology has transformed the computational capabilities in drug research by searching for solutions for complicated and tedious calculations. Quantum computing (QC) is exponentially more efficient in drug discovery, treatment, and therapeutics, generating profitable business for the pharmaceutical industry. In principle, it can be stated that quantum computing can solve complex problems exponentially faster than classical computing. Here it is needed to mention that QC will not be able to take on every task that classical computers perform—at least not now. It may be classical and quantum-coupled computational technologies combined with machine learning (ML) and artificial intelligence (AI) will solve each task in the future. This review is an overview of quantum computing, which may soon revolutionize the pharmaceutical industry in drug discovery.
References
[1] Mullard A. 2021 FDA approvals. Nature Reviews Drug Discovery 2022; 21(2): 83–88. doi: 10.1038/d41573-022-00001-9
[2] Lee H, Park D, Kim DS. Determinants of growth in prescription drug spending using 2010–2019 health insurance claims data. Frontiers in Pharmacology 2021; 12: 681492. doi: 10.3389/fphar.2021.681492
[3] Jackson M, McAdams S. The future of quantum drug discovery. Available online: https://medium.com/cambridge-quantum-computing/the-future-of-quantum-drug-discovery-909aa5140bff (accessed on 6 December 2023).
[4] Sengupta K, Srivastava PR. Quantum algorithm for quicker clinical prognostic analysis: An application and experimental study using CT scan images of COVID-19 patients. BMC Medical Informatics and Decision Making 2021; 21(1): 227. doi: 10.1186/s12911-021-01588-6
[5] Kais S. Introduction to quantum information and computation for chemistry. In: Kais S (editor). Quantum Information and Computation for Chemistry. John Wiley & Sons; 2014. pp. 1–38. doi: 10.1002/9781118742631
[6] Jordan S. The quantum algorithm zoo. Available online: http://math.nist.gov/quan tum/zoo/ (accessed on 6 December 2023).
[7] Szabo A, Ostlund NS. Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory. Collier Macmillan; 1982. 446p.
[8] Hanson DM, Harvey E, Sweeney R, Zielinski TJ. Quantum States of Atoms and Molecules. LibreTexts; 2022.
[9] Parr RG, Yang W. Density-Functional Theory of Atoms and Molecules. Oxford University Press; 1989. 333p. doi: 10.1093/oso/9780195092769.001.0001
[10] Mazziotti DA (editor). Reduced-Density-Matrix Mechanics: With Application to Many-Electron Atoms and Molecules. John Wiley & Sons; 2007. Volume 134. doi: 10.1002/0470106603
[11] Iachello F, Levine RD. Algebraic Theory of Molecules. Oxford University Press; 1995. doi: 10.1093/oso/9780195080919.001.0001
[12] Nightingale MP, Umrigar CJ (editors). Quantum Monte Carlo Methods in Physics and Chemistry, 1st ed. Springer Dordrecht; 1999. Volume 525. 467p.
[13] Evers M, Heid A, Ostojic I. Pharma’s digital Rx: Quantum computing in drug research and development. Available online: https://www.mckinsey.com/industries/life-sciences/our-insights/pharmas-digital-rx-quantum-computing-in-drug-research-and-development (accessed on 6 December 2023).
[14] Kadowaki T, Nishimori H. Quantum annealing in the transverse Ising model. Physical Review E 1998; 58(5): 5355–5363. doi: 10.1103/PhysRevE.58.5355
[15] Herschbach DR, Avery JS, Goscinski O (editors). Dimensional Scaling in Chemical Physics. Springer Dordrecht; 1993. 510p. doi: 10.1007/978-94-011-1836-1
[16] Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: Progress in machine intelligence for rational drug discovery. Drug Discovery Today 2017; 22(11): 1680–1685. doi: 10.1016/j.drudis.2017.08.010
[17] Wang S, Sun S, Li Z, et al. Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Computational Biology 2017; 13(1): e1005324. doi: 10.1371/journal.pcbi.1005324
[18] Wang S, Peng J, Ma J, Xu J. Protein secondary structure prediction using deep convolutional neural fields. Scientific Reports 2016; 6(1): 18962. doi: 10.1038/srep18962
[19] Evans R, Jumper J, Kirkpatrick J, et al. De novo structure prediction with deep-learning based scoring. In: Peoceedings of the 13th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction; December 1–4 2018; Riviera Maya. Protein Structure Prediction Center; 2018.
[20] Holm L, Rosenström P. Dali server: Conservation mapping in 3D. Nucleic Acids Research 2010; 38: W545–W549. doi: 10.1093/nar/gkq366
[21] Zhao Z, Fitzsimons JK, Osborne MA, et al. Quantum algorithms for training Gaussian processes. Physical Review A 2019; 100(1): 012304. doi: 10.1103/PhysRevA.100.012304
[22] Liu Y, Zhang S. Fast quantum algorithms for least squares regression and statistic leverage scores. Theoretical Computer Science 2017; 657: 38–47. doi: 10.1016/j.tcs.2016.05.044
[23] von Burg V, Low GH, Häner T, et al. Quantum computing enhanced computational catalysis. Physical Review Research 2021; 3(3): 033055. doi: 10.1103/PhysRevResearch.3.033055
[24] Sanders YR, Berry DW, Costa PC, et al. Compilation of fault-tolerant quantum heuristics for combinatorial optimization. PRX Quantum 2020; 1(2): 020312. doi: 10.1103/PRXQuantum.1.020312
[25] Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nature Reviews Genetics 2015; 16(6): 321–332. doi: 10.1038/nrg3920
[26] Ringnér M. What is principal component analysis? Nature Biotechnology 2008; 26(3): 303–304. doi: 10.1038/nbt0308-303
[27] Bishop CM. Pattern Recognition and Machine Learning. Springer; 2006. 738p.
[28] Kitaev AY. Quantum measurements and the Abelian stabilizer problem. Available online: https://arxiv.org/abs/quant-ph/9511026 (accessed on 6 December 2023).
[29] Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface 2018; 15(141): 20170387. doi: 10.1098/rsif.2017.0387
[30] Gómez-Bombarelli R, Wei JN, Duvenaud D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science 2018; 4(2): 268–276. doi: 10.1021/acscentsci.7b00572
[31] Smith JS, Isayev O, Roitberg AE. ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost. Chemical Science 2017; 8(4): 3192–3203. doi: 10.1039/C6SC05720A
[32] Harris SA, Kendon VM. Quantum-assisted biomolecular modelling. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2010; 368(1924): 3581–3592. doi: 10.1098/rsta.2010.0087
[33] Perdomo-Ortiz A, Dickson N, Drew-Brook M, et al. Finding low-energy conformations of lattice protein models by quantum annealing. Scientific Reports 2012; 2: 571. doi: 10.1038/srep00571
[34] Li RY, Di Felice R, Rohs R, Lidar DA. Quantum annealing versus classical machine learning applied to a simplified computational biology problem. NPJ Quantum Information 2018; 4(1): 14. doi: 10.1038/s41534-018-0060-8
[35] Chin AW, Datta A, Caruso F, et al. Noise-assisted energy transfer in quantum networks and light-harvesting complexes. New Journal of Physics 2010; 12(6): 065002. doi: 10.1088/1367-2630/12/6/065002
[36] Caruso F, Chin AW, Datta A, et al. Entanglement and entangling power of the dynamics in light-harvesting complexes. Physical Review A 2010; 81(6): 062346. doi: 10.1103/PhysRevA.81.062346
[37] Asadian A, Tiersch M, Guerreschi GG, et al. Motional effects on the efficiency of excitation transfer. New Journal of Physics 2010; 12(7): 075019. doi: 10.1088/1367-2630/12/7/075019
[38] Mohseni M, Rebentrost P, Lloyd S, Aspuru-Guzik A. Environment-assisted quantum walks in photosynthetic energy transfer. The Journal of Chemical Physics 2008; 129(17): 174106. doi: 10.1063/1.3002335
[39] Giorda P, Garnerone S, Zanardi P, Lloyd S. Interplay between coherence and decoherence in LHCII photosynthetic complex. Available online: https://arxiv.org/abs/1106.1986 (accessed on 6 December 2023).
[40] Dorner R, Goold J, Heaney L, et al. Quantum coherent contributions in biological electron transfer. Available online: https://arxiv.org/abs/1111.1646 (accessed on 6 December 2023).
[41] Dorner R, Goold J, Vedral V. Towards quantum simulations of biological information flow. Interface Focus 2012; 2(4): 522–528. doi: 10.1098/rsfs.2011.0109
[42] Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 1970; 48(3): 443–453. doi: 10.1016/0022-2836(70)90057-4
[43] Smith TF, Waterman MS. Identification of common molecular subsequences. Journal of Molecular Biology 1981; 147(1): 195–197. doi: 10.1016/0022-2836(81)90087-5
[44] Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 2010; 26(5): 589–595. doi: 10.1093/bioinformatics/btp698
[45] Dobin A, Davis CA, Schlesinger F, et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 2013; 29(1): 15–21. doi: 10.1093/bioinformatics/bts635
[46] Schuld M, Sinayskiy I, Petruccione F. An introduction to quantum machine learning. Contemporary Physics 2015; 56(2): 172–185. doi: 10.1080/00107514.2014.964942
[47] Srinivasan S, Downey C, Boots B. Learning and inference in Hilbert space with quantum graphical models. In: Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018); 2–8 December 2018; Montréal, Canada.
[48] Srinivasan S, Gordon G, Boots B. Learning hidden quantum Markov models. In: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018; 9–11 April 2018; Playa Blanca, Lanzarote, Canary Islands. Volume 84, pp. 1979–1987.
[49] Wang D, Liu S, Warrell J, et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 2018; 362(6420): eaat8464. doi: 10.1126/science.aat8464
[50] Ward LD, Kellis M. Interpreting noncoding genetic variation in complex traits and human disease. Nature Biotechnology 2012; 30(11): 1095–1106. doi: 10.1038/nbt.2422
[51] Gusev A, Ko A, Shi H, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nature Genetics 2016; 48(3): 245–252. doi: 10.1038/ng.3506
[52] Pasaniuc B, Price AL. Dissecting the genetics of complex traits using summary association statistics. Nature Reviews Genetics 2017; 18(2): 117–127. doi: 10.1038/nrg.2016.142
[53] Veis L, Višňák J, Fleig T, et al. Relativistic quantum chemistry on quantum computers. Physical Review A 2012; 85(3): 030304. doi: 10.1103/PhysRevA.85.030304
[54] Lippard SJ, Berg JM. Principles of Bioinorganic Chemistry. University Science Books; 1994. 450p.
[55] Batra K, Zorn KM, Foil DH, et al. Quantum machine learning algorithms for drug discovery applications. Journal of Chemical Information and Modeling 2021; 61(6): 2641–2647. doi: 10.1021/acs.jcim.1c00166
[56] Lau B, Emani PS, Chapman J, et al. Insights from incorporating quantum computing into drug design workflows. Bioinformatics 2023; 39(1): btac789. doi: 10.1093/bioinformatics/btac789
[57] Mustafa H, Morapakula SN, Jain P, Ganguly S. Variational quantum algorithms for chemical simulation and drug discovery. In: Proceedings of the 2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT); 13–15 October 2022; Pune, India. pp. 1–8. doi: 10.1109/TQCEBT54229.2022.10041453
[58] Robert A, Barkoutsos PK, Woerner S, Tavernelli I. Resource-efficient quantum algorithm for protein folding. npj Quantum Information 2021; 7(1): 38. doi: 10.48550/arXiv.1908.02163
[59] Merali Z. AlphaFold developers win US$3-million breakthrough prize. Available online: https://www.nature.com/articles/d41586-022-02999-9 (accessed on 6 December 2023).
[60] Dill KA, MacCallum JL. The protein-folding problem, 50 years on. Science 2012; 338(6110): 1042–1046. doi: 10.1126/science.1219021
[61] Dill KA. Theory for the folding and stability of globular proteins. Biochemistry 1985; 24(6): 1501–1509. doi: 10.1021/bi00327a032
[62] Lau KF, Dill KA. A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules 1989; 22(10): 3986–3997. doi: 10.1021/ma00200a030
[63] Miyazawa S, Jernigan RL. Estimation of effective interresidue contact energies from protein crystal structures: Quasi-chemical approximation. Macromolecules 1985; 18(3): 534–552. doi: 10.1021/ma00145a039
[64] Dill KA, Bromberg S, Yue K, et al. Principles of protein folding—A perspective from simple exact models. Protein Science 1995; 4(4): 561–602. doi: 10.1002/pro.5560040401
[65] Skolnick J, Kolinski A, Kihara D, et al. Ab initio protein structure prediction via a combination of threading, lattice folding, clustering, and structure refinement. Proteins: Structure, Function, and Bioinformatics 2001; 45(S5): 149–156. doi: 10.1002/prot.1172
[66] Hoque T, Chetty M, Sattar A. Extended HP model for protein structure prediction. Journal of Computational Biology 2009; 16(1): 85–103. doi: 10.1089/cmb.2008.0082
[67] Rohl CA, Strauss CE, Misura KM, Baker D. Protein structure prediction using Rosetta. In: Brand L, Johnson ML (editors). Methods in Enzymology. Academic Press; 2004. Volume 383. pp. 66–93. doi: 10.1016/S0076-6879(04)83004-0
[68] Marchand DJ, Noori M, Roberts A, et al. A variable neighbourhood descent heuristic for conformational search using a quantum annealer. Scientific Reports 2019; 9(1): 13708. doi: 10.1038/s41598-019-47298-y
[69] Jackson M. The future of quantum drug discovery. Available online: https://medium.com/cambridge-quantum-computing/the-future-of-quantum-drug-discovery-909aa5140bff (accessed on 6 December 2023).
[70] Mulligan VK, Melo H, Merritt HI, et al. Designing peptides on a quantum computer. Available online: https://www.biorxiv.org/content/10.1101/752485v2.full.pdf (accessed on 6 December 2023).
[71] Liu CY, Goan HS. Hybrid gate-based and annealing quantum computing for large-size Ising problems. Available online: https://arxiv.org/abs/2208.03283 (accessed on 6 December 2023).
[72] Steane A. The ion trap quantum information processor. Applied Physics B 1997; 64(6): 623–643. doi: 10.1007/s003400050225
[73] Devoret MH, Schoelkopf RJ. Superconducting circuits for quantum information: An outlook. Science 2013; 339(6124): 1169–1174. doi: 10.1126/science.1231930
[74] O’brien JL. Optical quantum computing. Science 2007; 318(5856): 1567–1570. doi: 10.1126/science.1142892
[75] Preskill J. Quantum computing in the NISQ era and beyond. Quantum 2018; 2: 79. doi: 10.22331/q-2018-08-06-79
[76] Wittek P. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Academic Press; 2014.
[77] Al-Rabadi AN. Reversible Logic Synthesis: From Fundamentals to Quantum Computing. Springer Berlin; 2012. 427p. doi: 10.1007/978-3-642-18853-4
[78] Biamonte J, Wittek P, Pancotti N, et al. Quantum machine learning. Nature 2017; 549(7671): 195–202. doi: 10.1038/nature23474
[79] Li JA, Dong D, Wei Z, et al. Quantum reinforcement learning during human decision-making. Nature Human Behaviour 2020; 4(3): 294–307. doi: 10.1038/s41562-019-0804-2
[80] Aïmeur E, Brassard G, Gambs S. Quantum speed-up for unsupervised learning. Machine Learning 2013; 90: 261–287. doi: 10.1007/s10994-012-5316-5
[81] Li Z, Liu X, Xu N, Du J. Experimental realization of a quantum support vector machine. Physical Review Letters 2015; 114(14): 140504. doi: 10.1103/PhysRevLett.114.140504
[82] Wan KH, Dahlsten O, Kristjánsson H, et al. Quantum generalisation of feedforward neural networks. npj Quantum Information 2017; 3(1): 36. doi: 10.1038/s41534-017-0032-4
[83] Havlíček V, Córcoles AD, Temme K, et al. Supervised learning with quantum-enhanced feature spaces. Nature 2019; 567(7747): 209–212. doi: 10.1038/s41586-019-0980-2
[84] Zhang Y, Ni Q. Recent advances in quantum machine learning. Quantum Engineering 2020; 2(1): e34. doi: 10.1002/que2.34
[85] Albarrán-Arriagada F, Retamal JC, Solano E, Lamata L. Measurement-based adaptation protocol with quantum reinforcement learning. Physical Review A 2018; 98(4): 042315. doi: 10.1103/PhysRevA.98.042315
[86] Cao Y, Romero J, Aspuru-Guzik A. Potential of quantum computing for drug discovery. IBM Journal of Research and Development 2018; 62(6): 6:1–6:20. doi: 10.1147/JRD.2018.2888987
[87] Broughton M, Verdon G, McCourt T, et al. Tensorflow quantum: A software framework for quantum machine learning. Available online: https://arxiv.org/abs/2003.02989 (accessed on 7 December 2023).
[88] Shor PW. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Review 1999; 41(2): 303–332. doi: 10.1137/S0036144598347011
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