Vol. 4 No. 1 (2024): Journal of Deep Learning in Genomic Data Analysis
Articles

AI-driven Drug Target Identification for Therapeutic Development

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  • Dr. Zara Mohammed
Dr. Zara Mohammed
Associate Professor of Computer Science, University of Baghdad, Iraq
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Published 31-07-2024

Keywords

  • AI,
  • Drug Discovery,
  • Drug Target Identification,
  • Therapeutic Development,
  • Machine Learning,
  • Deep Learning,
  • Network Analysis,
  • Computational Biology,
  • Bioinformatics
    • ...More
    Less
Creative Commons License

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

How to Cite

[1]
Dr. Zara Mohammed, “AI-driven Drug Target Identification for Therapeutic Development”, Journal of Deep Learning in Genomic Data Analysis, vol. 4, no. 1, pp. 94–103, Jul. 2024, Accessed: Nov. 24, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/19

Abstract

This research paper explores the application of artificial intelligence (AI) in drug discovery, specifically focusing on AI-driven drug target identification for therapeutic development. Traditional drug discovery processes are time-consuming and costly, often requiring extensive experimental validation. In contrast, AI offers a promising approach to streamline and enhance this process by analyzing large datasets to identify potential drug targets with higher efficiency and accuracy. This paper discusses various AI algorithms and techniques used in drug target identification, including machine learning, deep learning, and network analysis. Additionally, it examines the challenges and future prospects of AI-driven drug target identification in therapeutic development.

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