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Логотип заголовку сторінки

Біологія та біотехнології

August 18, 2023; Cambridge, UK: V Міжнародна науково-практична конференція «EDUCATION AND SCIENCE OF TODAY: INTERSECTORAL ISSUES AND DEVELOPMENT OF SCIENCES»


REVOLUTIONIZING NEW DRUG RESEARCH: THE ROLE OF AI AND MACHINE LEARNING IN THE DISCOVERY OF NEW ANTIBIOTICS


DOI
https://doi.org/10.36074/logos-18.08.2023.27
Опубліковано
29.08.2023

Анотація

Recent technological advances have revolutionized drug discovery. Artificial intelligence (AI) and machine learning (ML) are among the most important new tools being used to identify novel drug targets. This study examines the role of AI and ML in the discovery of new antibiotics, such as abaucin, a process that involves the identification and study of small molecules that could be used as drugs to treat a variety of diseases. It discusses how AI and ML are used to analyze large datasets, identify previously unknown abaucin targets, and study the molecular interactions of known abaucins. It also examines the challenges associated with using AI and ML for drug discovery, such as the need for large datasets and complexity of molecular interactions. Finally, this paper provides an outlook on the potential of AI and ML to revolutionize drug research in the future.

Посилання

  1. Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020. https://doi.org/10.1093/database/baaa010
  2. Gallagher, B. J. (2023). New superbug-killing antibiotic discovered using AI. BBC News. https://www.bbc.com/news/health-65709834
  3. Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021). Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular Diversity, 25(3),
  4. –1360. https://doi.org/10.1007/s11030-021-10217-3
  5. Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021). Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opinion on Drug Discovery, 16(9),
  6. –959. https://doi.org/10.1080/17460441.2021.1909567
  7. Liu, G., Catacutan, D. B., Rathod, K., Swanson, K., Jin, W., Mohammed, J. C., Chiappino-Pepe, A., Syed, S. A., Fragis, M., Rachwalski, K., Magolan, J., Surette, M. G., Coombes, B. K., Jaakkola, T., Barzilay, R., Collins, J. J., & Stokes, J. M. (2023). Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nature Chemical Biology. https://doi.org/10.1038/s41589-023-01349-8
  8. Mak, K.-K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773–780. https://doi.org/10.1016/j.drudis.2018.11.014
  9. Muster, W., Breidenbach, A., Fischer, H., Kirchner, S., Müller, L., & Pähler, A. (2008). Computational toxicology in drug development. Drug Discovery Today, 13(7-8),
  10. –310. https://doi.org/10.1016/j.drudis.2007.12.007
  11. Nguyen, M.-T., Nguyen, T., & Tran, T. (2022). Learning to discover medicines. International Journal of Data Science and Analytics. https://doi.org/10.1007/s41060-022-00371-8
  12. Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2020). Artificial intelligence in drug discovery and development. Drug Discovery Today. https://doi.org/10.1016/j.drudis.2020.10.010
  13. Vemula, D., Jayasurya, P., Sushmitha, V., Kumar, Y. N., & Bhandari, V. (2022). CADD, AI and ML in Drug Discovery: A Comprehensive Review. European Journal of Pharmaceutical Sciences, 106324. https://doi.org/10.1016/j.ejps.2022.106324