Article Sidebar
-
Drug Design, Molecular Simulation, Quantum Computing
Abstract
The development of quantum computing provides great opportunities in various fields, one of which is in drug design. This technology offers a way to model molecular interactions more accurately and efficiently compared to conventional methods. This research aims to explore the potential of quantum computing in designing new drugs that are more effective by accelerating and improving precision in molecular simulations. This study aims to identify and evaluate the ability of quantum computing to design more effective drug compounds, as well as to understand how quantum simulation can improve the efficiency of the drug development process. The research method used is quantum simulation to analyze the interaction between compounds and biological targets. The selected compounds were analyzed using quantum algorithms to calculate bond energy and molecular stability. The results of the simulation are then compared with conventional drug design methods. The results show that quantum computing can model molecular interactions with more precision and efficiency. Compounds selected using quantum methods showed higher effectiveness, with stronger binding energies and more stable biological interactions compared to drug designs using classical methods. Quantum computing shows great potential in the design of new, more effective drugs. Although technical challenges still exist, especially in terms of hardware and algorithms, this research shows that these technologies can speed up and improve the drug design process. Further research is needed to overcome these limitations and optimize the application of quantum computing in the pharmaceutical field.
Full text article
References
Abdullahi, M. (2023). Structure-based drug design, molecular dynamics simulation, ADMET, and quantum chemical studies of some thiazolinones targeting influenza neuraminidase. Journal of Biomolecular Structure and Dynamics, 41(23), 13829–13843. https://doi.org/10.1080/07391102.2023.2208225
Ajala, A. (2023). Natural product inhibitors as potential drug candidates against Alzheimer’s disease: Structural-based drug design, molecular docking, molecular dynamic simulation experiments, and ADMET predictions. Journal of the Indian Chemical Society, 100(5). https://doi.org/10.1016/j.jics.2023.100977
Akash, S. (2023a). Modified coptisine derivatives as an inhibitor against pathogenic Rhizomucor miehei, Mycolicibacterium smegmatis (Black Fungus), Monkeypox, and Marburg virus by molecular docking and molecular dynamics simulation-based drug design approach. Frontiers in Pharmacology, 14(Query date: 2024-12-07 08:18:39). https://doi.org/10.3389/fphar.2023.1140494
Akash, S. (2023b). Novel computational and drug design strategies for inhibition of monkeypox virus and Babesia microti: Molecular docking, molecular dynamic simulation and drug design approach by natural compounds. Frontiers in Microbiology, 14(Query date: 2024-12-07 08:18:39). https://doi.org/10.3389/fmicb.2023.1206816
Anand, A. (2022). A quantum computing view on unitary coupled cluster theory. Chemical Society Reviews, 51(5), 1659–1684. https://doi.org/10.1039/d1cs00932j
Ayipo, Y. O. (2022). Structure-Based Design and Molecular Simulations of Some Quercetin-Based Drugs as Repurposable Inhibitors of SARS-CoV-2 Main Protease. Trends in Sciences, 19(21). https://doi.org/10.48048/tis.2022.2557
Bardin, J. C. (2021). Microwaves in Quantum Computing. IEEE Journal of Microwaves, 1(1), 403–427. https://doi.org/10.1109/JMW.2020.3034071
Bayerstadler, A. (2021). Industry quantum computing applications. EPJ Quantum Technology, 8(1). https://doi.org/10.1140/epjqt/s40507-021-00114-x
Bova, F. (2021). Commercial applications of quantum computing. EPJ Quantum Technology, 8(1). https://doi.org/10.1140/epjqt/s40507-021-00091-1
Chen, L. (2023). Drug Design, Molecular Docking and Molecular Dynamics Simulations of Indole Class HIV-1 NNRTIs Explored with QSAR and Topomer Search. ChemistrySelect, 8(19). https://doi.org/10.1002/slct.202300759
Couteau, C. (2023). Applications of single photons to quantum communication and computing. Nature Reviews Physics, 5(6), 326–338. https://doi.org/10.1038/s42254-023-00583-2
Edache, E. I. (2024). Molecular phylogeny, Sequence-based drug design, Docking built virtual screening, dynamics simulations, and ADMET properties of thiazolino 2-pyridone amide derivatives as an inhibitor of Chlamydia trachomatis and SARS-CoV-2 protein. Turkish Computational and Theoretical Chemistry, 8(1), 10–39. https://doi.org/10.33435/TCANDTC.1196019
Fayyazi, N. (2022). Molecular dynamics simulation, 3D-pharmacophore and scaffold hopping analysis in the design of multi-target drugs to inhibit potential targets of COVID-19. Journal of Biomolecular Structure and Dynamics, 40(22), 11787–11808. https://doi.org/10.1080/07391102.2021.1965914
Hu, T. (2021). Movable oil content evaluation of lacustrine organic-rich shales: Methods and a novel quantitative evaluation model. Earth-Science Reviews, 214(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.earscirev.2021.103545
Ja’afaru, S. C. (2024). Unveiling potent inhibitors for schistosomiasis through ligand-based drug design, molecular docking, molecular dynamics simulations and pharmacokinetics predictions. PLoS ONE, 19(6). https://doi.org/10.1371/journal.pone.0302390
Ji, H. (2021). Qualitative and quantitative recognition method of drug-producing chemicals based on SnO2 gas sensor with dynamic measurement and PCA weak separation. Sensors and Actuators B: Chemical, 348(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.snb.2021.130698
Jones, J. (2024). The AI-driven Drug Design (AIDD) platform: An interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations. Journal of Computer-Aided Molecular Design, 38(1). https://doi.org/10.1007/s10822-024-00552-6
Jurcevic, P. (2021). Demonstration of quantum volume 64 on a superconducting quantum computing system. Quantum Science and Technology, 6(2). https://doi.org/10.1088/2058-9565/abe519
Kim, Y. (2023). Evidence for the utility of quantum computing before fault tolerance. Nature, 618(7965), 500–505. https://doi.org/10.1038/s41586-023-06096-3
Kwon, S. (2021). Gate-based superconducting quantum computing. Journal of Applied Physics, 129(4). https://doi.org/10.1063/5.0029735
Larsen, M. V. (2021). Deterministic multi-mode gates on a scalable photonic quantum computing platform. Nature Physics, 17(9), 1018–1023. https://doi.org/10.1038/s41567-021-01296-y
Leon, N. P. de. (2021). Materials challenges and opportunities for quantum computing hardware. Science, 372(6539). https://doi.org/10.1126/science.abb2823
Martínez, F. D. P. (2023). Molecular Simulation in Drug Design: An Overview of Molecular Dynamics Methods. Applied Computer-Aided Drug Design: Models and Methods, Query date: 2024-12-07 08:18:39, 202–257. https://doi.org/10.2174/9789815179934123010009
Mosteanu, N. R. (2021). Fintech frontiers in quantum computing, fractals, and blockchain distributed ledger: Paradigm shifts and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 1–19. https://doi.org/10.3390/joitmc7010019
Motta, M. (2022). Emerging quantum computing algorithms for quantum chemistry. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(3). https://doi.org/10.1002/wcms.1580
Mujal, P. (2021). Opportunities in Quantum Reservoir Computing and Extreme Learning Machines. Advanced Quantum Technologies, 4(8). https://doi.org/10.1002/qute.202100027
Nauta, M. (2023). From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ACM Computing Surveys, 55(13). https://doi.org/10.1145/3583558
Nokkala, J. (2021). Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing. Communications Physics, 4(1). https://doi.org/10.1038/s42005-021-00556-w
Pogorelov, I. (2021). Compact Ion-Trap Quantum Computing Demonstrator. PRX Quantum, 2(2). https://doi.org/10.1103/PRXQuantum.2.020343
Rossetti, G. (2024). How exascale computing can shape drug design: A perspective from multiscale QM/MM molecular dynamics simulations and machine learning-aided enhanced sampling algorithms. Current Opinion in Structural Biology, 86(Query date: 2024-12-07 08:18:39). https://doi.org/10.1016/j.sbi.2024.102814
Seidler, I. (2022). Conveyor-mode single-electron shuttling in Si/SiGe for a scalable quantum computing architecture. Npj Quantum Information, 8(1). https://doi.org/10.1038/s41534-022-00615-2
Shi, C. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. Eurasip Journal on Wireless Communications and Networking, 2021(1). https://doi.org/10.1186/s13638-021-01910-w
Teo, K. H. (2021). Emerging GaN technologies for power, RF, digital, and quantum computing applications: Recent advances and prospects. Journal of Applied Physics, 130(16). https://doi.org/10.1063/5.0061555
Tu, S. (2021). Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law. Catheterization and Cardiovascular Interventions, 97(Query date: 2024-12-01 09:57:11), 1040–1047. https://doi.org/10.1002/ccd.29592
Ullah, S. (2023). Validation of catalytic site residues of Ubiquitin Specific Protease 2 (USP2) by molecular dynamic simulation and novel kinetics assay for rational drug design. Molecular Diversity, 27(3), 1323–1332. https://doi.org/10.1007/s11030-022-10499-1
Umar, A. B. (2021). Ligand-based drug design and molecular docking simulation studies of some novel anticancer compounds on MALME-3M melanoma cell line. Egyptian Journal of Medical Human Genetics, 22(1). https://doi.org/10.1186/s43042-020-00126-9
Wu, Y. (2022). Erasure conversion for fault-tolerant quantum computing in alkaline earth Rydberg atom arrays. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-32094-6
Yang, Z. (2023). A Survey of Important Issues in Quantum Computing and Communications. IEEE Communications Surveys and Tutorials, 25(2), 1059–1094. https://doi.org/10.1109/COMST.2023.3254481
Zhou, F. (2023). Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation. ACS Omega, 8(20), 18312–18322. https://doi.org/10.1021/acsomega.3c02294
Zhu, F. (2024). Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems. Expert Systems with Applications, 236(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.eswa.2023.121219
Authors
Copyright (c) 2024 Aung Myint, Nandar Hlaing, Zaw Min Oo
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
