Vol. 4 No. 1 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

AI-driven Drug Repositioning for Identifying Novel Therapeutic Applications: Applies AI-driven approaches to repurpose existing drugs for new therapeutic indications, accelerating the discovery of potential treatments for various medical conditions

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  • Dr. Farzaneh Safaei
Dr. Farzaneh Safaei
Professor of Artificial Intelligence, Shahid Beheshti University, Iran
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Published 06-06-2024

Keywords

  • Drug repositioning,
  • artificial intelligence,
  • machine learning,
  • therapeutic applications,
  • drug discovery
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Farzaneh Safaei, “AI-driven Drug Repositioning for Identifying Novel Therapeutic Applications: Applies AI-driven approaches to repurpose existing drugs for new therapeutic indications, accelerating the discovery of potential treatments for various medical conditions”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 1, pp. 131–139, Jun. 2024, Accessed: Nov. 24, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/27

Abstract

Drug repositioning, also known as drug repurposing, is a strategy that aims to identify new therapeutic uses for existing drugs. This approach offers significant advantages over traditional drug development, including reduced costs and faster time to market. In recent years, the application of artificial intelligence (AI) in drug repositioning has emerged as a promising avenue for identifying novel therapeutic applications. AI-driven approaches leverage machine learning algorithms to analyze large-scale biological and chemical data, uncovering hidden relationships between drugs and diseases. This paper provides a comprehensive review of AI-driven drug repositioning, highlighting its principles, methodologies, and applications. We discuss the key challenges and opportunities in this field, as well as future directions for research and development.

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References

  1. Maruthi, Srihari, et al. "Deconstructing the Semantics of Human-Centric AI: A Linguistic Analysis." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 11-30.
  2. Dodda, Sarath Babu, et al. "Ethical Deliberations in the Nexus of Artificial Intelligence and Moral Philosophy." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 31-43.
  3. Zanke, Pankaj. "AI-Driven Fraud Detection Systems: A Comparative Study across Banking, Insurance, and Healthcare." Advances in Deep Learning Techniques 3.2 (2023): 1-22.
  4. Biswas, A., and W. Talukdar. “Robustness of Structured Data Extraction from In-Plane Rotated Documents Using Multi-Modal Large Language Models (LLM)”. Journal of Artificial Intelligence Research, vol. 4, no. 1, Mar. 2024, pp. 176-95, https://thesciencebrigade.com/JAIR/article/view/219.
  5. Maruthi, Srihari, et al. "Toward a Hermeneutics of Explainability: Unraveling the Inner Workings of AI Systems." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 27-44.
  6. Biswas, Anjanava, and Wrick Talukdar. "Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation." arXiv preprint arXiv:2405.18346 (2024).
  7. Yellu, Ramswaroop Reddy, et al. "AI Ethics-Challenges and Considerations: Examining ethical challenges and considerations in the development and deployment of artificial intelligence systems." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 9-16.
  8. Maruthi, Srihari, et al. "Automated Planning and Scheduling in AI: Studying automated planning and scheduling techniques for efficient decision-making in artificial intelligence." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 14-25.
  9. Ambati, Loknath Sai, et al. "Impact of healthcare information technology (HIT) on chronic disease conditions." MWAIS Proc 2021 (2021).
  10. Singh, Amarjeet, and Alok Aggarwal. "Securing Microservice CICD Pipelines in Cloud Deployments through Infrastructure as Code Implementation Approach and Best Practices." Journal of Science & Technology 3.3 (2022): 51-65.
  11. Zanke, Pankaj. "Enhancing Claims Processing Efficiency Through Data Analytics in Property & Casualty Insurance." Journal of Science & Technology 2.3 (2021): 69-92.
  12. Pulimamidi, R., and G. P. Buddha. "Applications of Artificial Intelligence Based Technologies in The Healthcare Industry." Tuijin Jishu/Journal of Propulsion Technology 44.3: 4513-4519.
  13. Dodda, Sarath Babu, et al. "Conversational AI-Chatbot Architectures and Evaluation: Analyzing architectures and evaluation methods for conversational AI systems, including chatbots, virtual assistants, and dialogue systems." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 13-20.
  14. Ponnusamy, Sivakumar, and Dinesh Eswararaj. "Modernization of Legacy Applications and Data: A Comprehensive Review on Implementation Challenges, Effective Strategies and Best Practices." (2024): 81-106.
  15. Maruthi, Srihari, et al. "Language Model Interpretability-Explainable AI Methods: Exploring explainable AI methods for interpreting and explaining the decisions made by language models to enhance transparency and trustworthiness." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 1-9.
  16. Dodda, Sarath Babu, et al. "Federated Learning for Privacy-Preserving Collaborative AI: Exploring federated learning techniques for training AI models collaboratively while preserving data privacy." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 13-23.
  17. Zanke, Pankaj. "Machine Learning Approaches for Credit Risk Assessment in Banking and Insurance." Internet of Things and Edge Computing Journal 3.1 (2023): 29-47.
  18. Maruthi, Srihari, et al. "Temporal Reasoning in AI Systems: Studying temporal reasoning techniques and their applications in AI systems for modeling dynamic environments." Journal of AI-Assisted Scientific Discovery 2.2 (2022): 22-28.
  19. Yellu, Ramswaroop Reddy, et al. "Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems." Hong Kong Journal of AI and Medicine 2.2 (2022): 12-20.
  20. Reddy Yellu, R., et al. "Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems. Hong Kong Journal of AI and Medicine, 2 (2), 12-20." (2022).
  21. Zanke, Pankaj, and Dipti Sontakke. "Artificial Intelligence Applications in Predictive Underwriting for Commercial Lines Insurance." Advances in Deep Learning Techniques 1.1 (2021): 23-38.
  22. Singh, Amarjeet, and Alok Aggarwal. "Artificial Intelligence based Microservices Pod configuration Management Systems on AWS Kubernetes Service." Journal of Artificial Intelligence Research 3.1 (2023): 24-37.
  23. 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.

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