Artificial Intelligence And Machine Learning In Drug Discovery
DOI:
https://doi.org/10.47611/jsrhs.v13i2.6572Keywords:
Machine Learning, Pharmaceutical Research, Drug Discovery, Neural NetworkAbstract
In contemporary pharmaceutical research, Machine Learning (ML) has emerged as a transformative force, profoundly impacting drug discovery and development. ML empowers computer systems to acquire knowledge from data, distinguishing itself through supervised and unsupervised learning. ML is pivotal in pharmaceutical research, enhancing drug efficacy, ensuring safety, personalizing medical interventions, and expediting drug repurposing. As we peer into the horizon, we anticipate refinements through deep learning models and generative networks in drug discovery. Initiatives that promote data-sharing and collaborative partnerships will shape the ML-driven landscape. This academic exploration underscores ML's transformative role in pharmaceutical research, encompassing fundamental principles and practical applications. It embodies the convergence of technology and healthcare, promising an innovative and improved healthcare future.
Downloads
References or Bibliography
Ambadipudi, R. (n.d.). Council Post: How Machine Learning Will Transform Your Industry. Forbes. Retrieved September 2, 2023, from https://www.forbes.com/sites/forbestechcouncil/2023/02/27/how-machine-learning-will-transform-your-industry/
Artificial intelligence and machine learning in drug discovery and... (n.d.). ResearchGate. Retrieved September 22, 2023, from https://www.researchgate.net/figure/Artificial-intelligence-and-machine-learning-in-drug-discovery-and-development-Gupta-et_fig3_369041775
Ayyadevara, K. (2018). Basics of Machine Learning.
Bajorath, J. (2015). Computer-aided drug discovery. F1000Research, 4, F1000 Faculty Rev-630. https://doi.org/10.12688/f1000research.6653.1
Hua, W., Cuiqin, M., & Lijuan, Z. (n.d.). A Brief Review of Machine Learning and its Application.
Jiao, Z., Hu, P., Xu, H., & Wang, Q. (2020). Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications. ACS Chemical Health & Safety, 27(6), 316–334. https://doi.org/10.1021/acs.chas.0c00075
Machine learning, explained | MIT Sloan. (2023, August 23). https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained
Réda, C., Kaufmann, E., & Delahaye-Duriez, A. (2020). Machine learning applications in drug development. Computational and Structural Biotechnology Journal, 18, 241–252. https://doi.org/10.1016/j.csbj.2019.12.006
Samek, W., & Montavon, G. (n.d.). Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications.
Shankar, S., & Zare, R. N. (2022). The perils of machine learning in designing new chemicals and materials. Nature Machine Intelligence, 4(4), Article 4. https://doi.org/10.1038/s42256-022-00481-9
Unterthiner, T., & Mayr, A. (n.d.). Deep Learning as an Opportunity in Virtual Screening.
Wale, N. (n.d.). Machine learning in drug discovery and development. 16 December 2010, 8.
What are Neural Networks? | IBM. (n.d.). Retrieved September 2, 2023, from https://www.ibm.com/topics/neural-networks
What is a Neural Network? (n.d.). TIBCO Software. Retrieved September 10, 2023, from https://www.tibco.com/reference-center/what-is-a-neural-network
What is Deep Learning? | IBM. (n.d.). Retrieved September 2, 2023, from https://www.ibm.com/topics/deep-learning
Published
How to Cite
Issue
Section
Copyright (c) 2024 Justin Gu; Connor Taylor
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.