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

AI-driven Drug Target Identification for Therapeutic Development

Dr. Zara Mohammed
Associate Professor of Computer Science, University of Baghdad, Iraq
Cover

Published 31-07-2024

Keywords

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

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. 13, 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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References

  1. Sadhu, Ashok Kumar Reddy. "Enhancing Healthcare Data Security and User Convenience: An Exploration of Integrated Single Sign-On (SSO) and OAuth for Secure Patient Data Access within AWS GovCloud Environments." Hong Kong Journal of AI and Medicine 3.1 (2023): 100-116.
  2. Jahangir, Zeib, et al. "Applications of ML and DL Algorithms in The Prediction, Diagnosis, and Prognosis of Alzheimer’s Disease." American Journal of Biomedical Science & Research 22.6 (2024): 779-786.
  3. Ahmad, Ahsan, et al. "Prediction of Fetal Brain and Heart Abnormalties using Artificial Intelligence Algorithms: A Review." American Journal of Biomedical Science & Research 22.3 (2024): 456-466.
  4. Shiwlani, Ashish, et al. "BI-RADS Category Prediction from Mammography Images and Mammography Radiology Reports Using Deep Learning: A Systematic Review." Jurnal Ilmiah Computer Science 3.1 (2024): 30-49.
  5. Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
  6. Perumalsamy, Jegatheeswari, Manish Tomar, and Selvakumar Venkatasubbu. "Advanced Analytics in Actuarial Science: Leveraging Data for Innovative Product Development in Insurance." Journal of Science & Technology 4.3 (2023): 36-72.
  7. Selvaraj, Amsa, Munivel Devan, and Kumaran Thirunavukkarasu. "AI-Driven Approaches for Test Data Generation in FinTech Applications: Enhancing Software Quality and Reliability." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 397-429.
  8. Katari, Monish, Selvakumar Venkatasubbu, and Gowrisankar Krishnamoorthy. "Integration of Artificial Intelligence for Real-Time Fault Detection in Semiconductor Packaging." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.3 (2023): 473-495.
  9. Tatineni, Sumanth, and Naga Vikas Chakilam. "Integrating Artificial Intelligence with DevOps for Intelligent Infrastructure Management: Optimizing Resource Allocation and Performance in Cloud-Native Applications." Journal of Bioinformatics and Artificial Intelligence 4.1 (2024): 109-142.
  10. Prakash, Sanjeev, et al. "Achieving regulatory compliance in cloud computing through ML." AIJMR-Advanced International Journal of Multidisciplinary Research 2.2 (2024).
  11. Reddy, Sai Ganesh, et al. "Harnessing the Power of Generative Artificial Intelligence for Dynamic Content Personalization in Customer Relationship Management Systems: A Data-Driven Framework for Optimizing Customer Engagement and Experience." Journal of AI-Assisted Scientific Discovery 3.2 (2023): 379-395.