Exploring the Frontiers of Drug Discovery with Machine Learning: An In-Depth Examination of Applications, Hurdles, and Future Trajectories

Priyanka Kapoor

Abstract


Machine Learning has emerged as a game-changer in the realm of drug discovery, offering novel approaches to accelerate research and development. This paper conducts a comprehensive analysis, delving into the wide-ranging applications, challenges, and future directions of machine learning in drug discovery. Through an in-depth exploration, it elucidates how machine learning is transforming target identification, compound screening, and drug repurposing. This research underscores the potential of machine learning to revolutionize the pharmaceutical industry and expedite the delivery of life-saving drugs to patients.

References


Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209.

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219.

Sheth, J. (2017). Chatbots as AI interfaces to business. Big Data, 5(1), 6-14.

Kunduru, A. R. (2023). Industry best practices on implementing oracle cloud ERP security. International Journal of Computer Trends and Technology, 71(6), 1-8. https://doi.org/10.14445/22312803/IJCTT-V71I6P101

Kunduru, A. R. (2023). Cloud Appian BPM (Business Process Management) Usage In health care Industry. IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, 12(6), 339-343. https://doi.org/10.17148/IJARCCE.2023.12658

WHIG, P. (2023). Blockchain Revolution: Innovations, Challenges, and Future Directions. International Journal of Machine Learning for Sustainable Development, 5(3), 16-25.

Whig, P., Kouser, S., Bhatia, A. B., Nadikattu, R. R., & Sharma, P. (2023). Explainable Machine Learning in Healthcare. In Explainable Machine Learning for Multimedia Based Healthcare Applications (pp. 77-98). Cham: Springer International Publishing.

Whig, P., Velu, A., Nadikattu, R. R., & Alkali, Y. J. (2023). Computational Science Role in Medical and Healthcare‐Related Approach. Handbook of Computational Sciences: A Multi and Interdisciplinary Approach, 245-272.

Kunduru, A. R. (2023). Effective usage of artificial intelligence in enterprise resource planning applications. International Journal of Computer Trends and Technology, 71(4), 73-80. https://doi.org/10.14445/22312803/IJCTT-V71I4P109

Kunduru, A. R. (2023). Recommendations to advance the cloud data analytics and chatbots by using machine learning technology. International Journal of Engineering and Scientific Research, 11(3), 8-20.

WHIG, P. (2023). A Comprehensive Review of Mask Detection Using Artificial Intelligence: Methods, Challenges, and Applications. International Journal of Sustainable Development in Computing Science, 5(2), 11-20.

Kunduru, A. R. (2023). Security concerns and solutions for enterprise cloud computing applications. Asian Journal of Research in Computer Science, 15(4), 24–33. https://doi.org/10.9734/ajrcos/2023/v15i4327

Sharma, A., Kumar, A., & Whig, P. (2015b). On the performance of CDTA based novel analog inverse low pass filter using 0.35 µm CMOS parameter. International Journal of Science, Technology & Management, 4(1), 594–601.

Tomar, U., Chakroborty, N., Sharma, H., & Whig, P. (2021). AI based Smart Agricuture System. Transactions on Latest Trends in Artificial Intelligence, 2(2).

Velu, A., & Whig, P. (2021a). Protect Personal Privacy And Wasting Time Using Nlp: A Comparative Approach Using Ai. Vivekananda Journal of Research, 10, 42–52.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 International Journal of Machine Learning for Sustainable Development

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Impact Factor : 

JCR Impact Factor: 5.9 (2020)

JCR Impact Factor: 6.1 (2021)

JCR Impact Factor: 6.7 (2022)

JCR Impact Factor: Under Evaluation (2023)

A Double-Blind Peer Reviewed Refereed Journal