Transforming Drug Discovery: Leveraging Deep Learning and NLP for Accelerated Drug Repurposing through Text Mining in Biomedical Literature

Authors

  • Abhishek Thakur Harrisburg University of Science & Technology, Harrisburg, PA– 17101, USA
  • Gopal Kumar Thakur Harrisburg University of Science & Technology, Harrisburg, PA– 17101, USA
  • Naseebia Khan Harrisburg University of Science & Technology, Harrisburg, PA– 17101, USA
  • Shridhar Kulkarni Harrisburg University of Science & Technology, Harrisburg, PA– 17101, USA

Keywords:

drug repurposing, drug repositioning, text mining, deep learning, natural language processing, biomedical literature, drug discovery

Abstract

Drug repurposing or drug repositioning aims to find new uses for existing drugs, providing a faster and more cost-effective approach to drug development compared to traditional methods. Rapid advancements in deep learning and natural language processing (NLP) methods present new opportunities to accelerate drug repurposing through automated analysis of biomedical literature. This paper provides a comprehensive review of recent applications of deep learning and NLP for drug repurposing through text mining of biomedical corpora. We describe the motivations, challenges, and trends in this exciting field, summarize key techniques, and present illustrative case studies. Promising directions for continued research are also discussed. Overall, this paper demonstrates how deep learning and NLP are transforming drug discovery by enabling large-scale mining of biomedical text to uncover hidden relationships between drugs, diseases, targets, and mechanisms.

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Published

24.03.2024

How to Cite

Thakur, A. ., Thakur, G. K. ., Khan, N. ., & Kulkarni, S. . (2024). Transforming Drug Discovery: Leveraging Deep Learning and NLP for Accelerated Drug Repurposing through Text Mining in Biomedical Literature . International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 165–172. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5128

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Research Article