Authors : Joseph Kobi; Amida Nchaw Nchaw

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/466wdecu

DOI : https://doi.org/10.38124/ijisrt/25may2204

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The integration of cloud computing technologies with molecular modeling approaches has revolutionized the landscape of breast cancer drug discovery. The article examines the levels of impact that cloud computing would have had in speeding breast cancer-specific therapeutic compound discovery, virtual screening, and optimization. Using distributed computing frameworks, researchers can now conduct sophisticated molecular simulations, quantum calculations and machine learning algorithms many times faster than earlier. This paper explores a range of cloud computation methods used in breast cancer work, including infrastructure-as-a-service solutions, container systems, and molecular modeling tools adapted for cloud environments. It is evident that computational efficiency gains emerged; the investigation demonstrated significant drops on time spent for molecular docking, molecular dynamics simulations, and QSAR investigations. Furthermore, the research highlights how cloud-based collaborative tools improve inter-team data exchange, resulting in accelerated breast cancer therapy developments from target identification to lead optimization. The development of tailored breast cancer therapies has a lot to gain from innovations in cloud-based multi-omics methods. By extensively analysing the use of the cloud to compute in breast cancer drug discovery, this investigation shows tremendous speed-acceleration possibilities using modern technologies such as quantum computing and federated learning systems.

Keywords : Cloud Computing, Breast Cancer, Drug Discovery, Molecular Modeling, High-Performance Computing, Virtual Screening.

References :

1. Adelusi, T. I., Oyedele, A. Q. K., Boyenle, I. D., Ogunlana, A. T., Adeyemi, R. O., Ukachi, C. D., ... & Abdul-Hammed, M. (2022). Molecular modeling in drug discovery. Informatics in Medicine Unlocked, 29, 100880. https://www.sciencedirect.com/science/article/pii/S235291482200034X
2. Korb, O., Finn, P. W., & Jones, G. (2014). The cloud and other new computational methods to improve molecular modelling. Expert opinion on drug discovery, 9(10), 1121-1131. https://www.tandfonline.com/doi/abs/10.1517/17460441.2014.941800
3. Banegas-Luna, A. J., Imbernon, B., Llanes Castro, A., Perez-Garrido, A., Ceron-Carrasco, J. P., Gesing, S., ... & Perez-Sanchez, H. (2019). Advances in distributed computing with modern drug discovery. Expert opinion on drug discovery, 14(1), 9-22. https://www.tandfonline.com/doi/abs/10.1080/17460441.2019.1552936
4. Subramanian, G., & Ramamoorthy, K. (2024). From Data to Diagnosis Exploring AWS Cloud Solutions in Multi-Omics Breast Cancer Biomarker Research. Computational Biology and Bioinformatics, 12(1), 1-11. http://www.cbbj.org/article/10.11648/j.cbb.20241201.11
5. Karampuri, A., Kundur, S., & Perugu, S. (2024). Exploratory drug discovery in breast cancer patients: a multimodal deep learning approach to identify novel drug candidates targeting RTK signaling. Computers in Biology and Medicine, 174, 108433. https://www.sciencedirect.com/science/article/pii/S0010482524005171
6. Niazi, S. K., & Mariam, Z. (2023). Computer-aided drug design and drug discovery: a prospective analysis. Pharmaceuticals, 17(1), 22. https://www.mdpi.com/1424-8247/17/1/22
7. Bozorgpour, R., Sheybanikashani, S., & Mohebi, M. (2023). Exploring the role of molecular dynamics simulations in most recent cancer research: Insights into treatment strategies. arXiv preprint arXiv:2310.19950. https://arxiv.org/abs/2310.19950
8. Polineni, T. N. S. (2024). Integrating Quantum Computing and Big Data Analytics for Accelerated Drug Discovery: A New Paradigm in Healthcare Innovation. Journal of Artificial Intelligence and Big Data Disciplines, 1(1), 38-49. https://jaibdd.com/index.php/jaibdd/article/view/5
9. Herráiz-Gil, S., Nygren-Jiménez, E., Acosta-Alonso, D. N., León, C., & Guerrero-Aspizua, S. (2021). Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer. Applied Sciences, 15(5), 2798. https://www.mdpi.com/2076-3417/15/5/2798
10. Prieto-Martínez, F. D., López-López, E., Juárez-Mercado, K. E., & Medina-Franco, J. L. (2019). Computational drug design methods—current and future perspectives. In silico drug design, 19-44. https://www.sciencedirect.com/science/article/pii/B978012816125800002X
11. Rehan, H. (2024). Advancing Cancer Treatment with AI-Driven Personalized Medicine and Cloud-Based Data Integration. Journal of Machine Learning in Pharmaceutical Research, 4(2), 1-40.
12. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., ... & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature reviews Drug discovery, 18(6), 463-477.
13. Zhou, H., Liu, H., Yu, Y., Yuan, X., & Xiao, L. (2023). Informatics on drug repurposing for breast cancer. Drug Design, Development and Therapy, 1933-1943. https://www.tandfonline.com/doi/abs/10.2147/DDDT.S417563
14. Qian, T., Zhu, S., & Hoshida, Y. (2019). Use of big data in drug development for precision medicine: an update. Expert review of precision medicine and drug development, 4(3), 189-200. https://www.tandfonline.com/doi/abs/10.1080/23808993.2019.1617632
15. Choudhuri, S., Yendluri, M., Poddar, S., Li, A., Mallick, K., Mallik, S., & Ghosh, B. (2023). Recent advancements in computational drug design algorithms through machine learning and optimization. Kinases and Phosphatases, 1(2), 117-140. https://www.mdpi.com/2813-3757/1/2/8
16. Khanfar, M. A., Youssef, D. T., & El Sayed, K. A. (2010). Semisynthetic latrunculin derivatives as inhibitors of metastatic breast cancer: biological evaluations, preliminary structure–activity relationship and molecular modeling studies. ChemMedChem: Chemistry Enabling Drug Discovery, 5(2), 274-285. https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/cmdc.200900430
17. Mehmood, A., Nawab, S., Jin, Y., Hassan, H., Kaushik, A. C., & Wei, D. Q. (2023). Ranking breast cancer drugs and biomarkers identification using machine learning and pharmacogenomics. ACS Pharmacology & Translational Science, 6(3), 399-409. https://pubs.acs.org/doi/abs/10.1021/acsptsci.2c00212
18. Margolin, A. A., Bilal, E., Huang, E., Norman, T. C., Ottestad, L., Mecham, B. H., ... & Børresen-Dale, A. L. (2013). Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer. Science translational medicine, 5(181), 181re1-181re1. https://www.science.org/doi/abs/10.1126/scitranslmed.3006112
19. Margolin, A. A., Bilal, E., Huang, E., Norman, T. C., Ottestad, L., Mecham, B. H., ... & Børresen-Dale, A. L. (2013). Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer. Science translational medicine, 5(181), 181re1-181re1. https://www.science.org/doi/abs/10.1126/scitranslmed.3006112
20. Guillen, K. P., Fujita, M., Butterfield, A. J., Scherer, S. D., Bailey, M. H., Chu, Z., ... & Welm, A. L. (2022). A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology. Nature cancer, 3(2), 232-250. https://www.nature.com/articles/s43018-022-00337-6
21. Ahmad, S., Sheikh, K., Bano, N., Rafeeq, M. M., Mohammed, M. R. S., Yadav, M. K., & Raza, K. (2022). Illustrious implications of nature-inspired computing methods in therapeutics and computer-aided drug design. In Nature-inspired intelligent computing techniques in bioinformatics (pp. 293-308). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-19-6379-7_15
22. Qureshi, R., Zou, B., Alam, T., Wu, J., Lee, V. H., & Yan, H. (2022). Computational methods for the analysis and prediction of egfr-mutated lung cancer drug resistance: Recent advances in drug design, challenges and future prospects. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(1), 238-255. https://ieeexplore.ieee.org/abstract/document/9676451/
23. Sahlgren, C., Meinander, A., Zhang, H., Cheng, F., Preis, M., Xu, C., ... & Sandler, N. (2017). Tailored approaches in drug development and diagnostics: from molecular design to biological model systems. Advanced Healthcare Materials, 6(21), 1700258. https://onlinelibrary.wiley.com/doi/abs/10.1002/adhm.201700258
24. Zhang, P., & Brusic, V. (2014). Mathematical modeling for novel cancer drug discovery and development. Expert opinion on drug discovery, 9(10), 1133-1150. https://www.tandfonline.com/doi/abs/10.1517/17460441.2014.941351
25. Carabet, L. A., Rennie, P. S., & Cherkasov, A. (2018). Therapeutic inhibition of Myc in cancer. Structural bases and computer-aided drug discovery approaches. International journal of molecular sciences, 20(1), 120. https://www.mdpi.com/1422-0067/20/1/120
26. Sukumaran, S., Zochedh, A., Chandran, K., Sultan, A. B., & Kathiresan, T. (2024). Exploring the co‐activity of FDA approved drug gemcitabine and docetaxel for enhanced anti‐breast cancer activity: DFT, docking, molecular dynamics simulation and pharmacophore studies. International Journal of Quantum Chemistry, 124(4), e27359. https://onlinelibrary.wiley.com/doi/abs/10.1002/qua.27359
27. Salo-Ahen, O. M., Alanko, I., Bhadane, R., Bonvin, A. M., Honorato, R. V., Hossain, S., ... & Vanmeert, M. (2020). Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes, 9(1), 71. https://www.mdpi.com/2227-9717/9/1/71
28. Beg, A., & Parveen, R. (2021). Role of bioinformatics in cancer research and drug development. In Translational bioinformatics in healthcare and medicine (pp. 141-148). Academic Press. https://www.sciencedirect.com/science/article/pii/B9780323898249000112
29. Sayal, A., Jha, J., Chaithra, N., Gangodkar, A. R., & Shaziya Banu, S. (2024). Revolutionizing Drug Discovery: The Role of AI and Machine Learning in Accelerating Medicinal Advancements. Artificial Intelligence and Machine Learning in Drug Design and Development, 189-221.
30. Marques, L., Costa, B., Pereira, M., Silva, A., Santos, J., Saldanha, L., ... & Vale, N. (2024). Advancing precision medicine: a review of innovative in silico approaches for drug development, clinical pharmacology and personalized healthcare. Pharmaceutics, 16(3), 332.
31. Husnain, A., Rasool, S., Saeed, A., & Hussain, H. K. (2023). Revolutionizing pharmaceutical research: Harnessing machine learning for a paradigm shift in drug discovery. International Journal of Multidisciplinary Sciences and Arts, 2(4), 149-157. https://www.neliti.com/publications/591860/revolutionizing-pharmaceutical-research-harnessing-machine-learning-for-a-paradi
32. Afrose, N., Chakraborty, R., Hazra, A., Bhowmick, P., & Bhowmick, M. (2024). AI-Driven Drug Discovery and Development. In Future of AI in Biomedicine and Biotechnology (pp. 259-277). IGI Global. https://www.igi-global.com/chapter/ai-driven-drug-discovery-and-development/348519
33. Hinkson, I. V., Davidsen, T. M., Klemm, J. D., Chandramouliswaran, I., Kerlavage, A. R., & Kibbe, W. A. (2017). A comprehensive infrastructure for big data in cancer research: accelerating cancer research and precision medicine. Frontiers in cell and developmental biology, 5, 83. https://www.frontiersin.org/articles/10.3389/fcell.2017.00083/full
34. Shahab, M., Zheng, G., Khan, A., Wei, D., & Novikov, A. S. (2023). Machine learning-based virtual screening and molecular simulation approaches identified novel potential inhibitors for cancer therapy. Biomedicines, 11(8), 2251. https://www.mdpi.com/2227-9059/11/8/2251
35. Karuppasamy, J., Zochedh, A., Al-Asbahi, B. A., Kumar, Y. A., Shunmuganarayanan, A., & Sultan, A. B. (2024). Combined experimental and quantum computational studies on 5-Fluorouracil salicylic acid cocrystal: Assessment on anti-breast cancer potential through docking simulation. Journal of Molecular Structure, 1311, 138406.
36. Zochedh, A., Chandran, K., Shunmuganarayanan, A., & Sultan, A. B. (2024). Exploring the synergistic effect of tegafur-syringic acid adduct against breast cancer through DFT computation, spectroscopy, pharmacokinetics and molecular docking simulation. Polycyclic Aromatic Compounds, 44(4), 2153-2187. https://www.tandfonline.com/doi/abs/10.1080/10406638.2023.2214281
37. Metibemu, D. S., & Ogungbe, I. V. (2022). Carotenoids in drug discovery and medicine: pathways and molecular targets implicated in human diseases. Molecules, 27(18), 6005. https://www.mdpi.com/1420-3049/27/18/6005
38. Pandey, A. K., & Verma, S. (2024). Computational Approaches for Structure-Assisted Drug Discovery and Repurposing. In Unraveling New Frontiers and Advances in Bioinformatics (pp. 163-192). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-97-7123-3_9.