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Using a variety of computer-based methods, computational drug repurposing finds novel therapeutic uses for well-known pharmaceuticals. An improved and more affordable option to traditional drug discovery is computational drug repurposing. Using medications that have undergone clinical validation minimizes the risk of failure and temporal and financial investments on preclinical and clinical testing. This chapter explores many methodologies in this field, including genome-wide association studies, knowledge-based repurposing, and electronic health record analysis, in detail. It encompasses a wide range of techniques, from applying sophisticated algorithms like machine learning to comprehending the structure of molecules. The chapter gives convincing instances of how computational approaches have been applied to find novel applications for already-approved medications. In particular, the chapter explores cases where this strategy has worked to treat diseases like COVID-19 and cancer, demonstrating the revolutionary potential of computational drug repurposing to completely change the drug discovery process. A major obstacle in the realm of computational medication repurposing is striking a delicate balance between carrying out in-depth data analysis and guaranteeing the highest level of patient privacy protection. The chapter emphasizes the significance of preserving data quality, fostering collaborative efforts, and validating the accuracy of computational models. Additionally, the authors discuss views on the difficulties caused by biased data, the scarcity of resources, and the moral issues involved with utilizing such data for important decision-making processes. In essence, this chapter provides a comprehensive discussion of computational drug repurposing in a way that is both widely understood and adheres to the high standards of scholarly integrity that are expected in the scientific domain. It provides an example of how this latest approach can quickly identify novel therapeutic uses for well-known medications, advancing the field of drug discovery.

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Drug Repurposing-1

  • 章节

  • 发表日期：

    2025年

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  • Bhanu Teja Korra,
  • Kunjulakshmi Roffin,
  • Subashani Singh,
  • Sushree Sangita Kar,
  • Kavita Kundal,
  • Avik Sengupta,
  • Rahul Kumar

摘要

Using a variety of computer-based methods, computational drug repurposing finds novel therapeutic uses for well-known pharmaceuticals. An improved and more affordable option to traditional drug discovery is computational drug repurposing. Using medications that have undergone clinical validation minimizes the risk of failure and temporal and financial investments on preclinical and clinical testing. This chapter explores many methodologies in this field, including genome-wide association studies, knowledge-based repurposing, and electronic health record analysis, in detail. It encompasses a wide range of techniques, from applying sophisticated algorithms like machine learning to comprehending the structure of molecules. The chapter gives convincing instances of how computational approaches have been applied to find novel applications for already-approved medications. In particular, the chapter explores cases where this strategy has worked to treat diseases like COVID-19 and cancer, demonstrating the revolutionary potential of computational drug repurposing to completely change the drug discovery process. A major obstacle in the realm of computational medication repurposing is striking a delicate balance between carrying out in-depth data analysis and guaranteeing the highest level of patient privacy protection. The chapter emphasizes the significance of preserving data quality, fostering collaborative efforts, and validating the accuracy of computational models. Additionally, the authors discuss views on the difficulties caused by biased data, the scarcity of resources, and the moral issues involved with utilizing such data for important decision-making processes. In essence, this chapter provides a comprehensive discussion of computational drug repurposing in a way that is both widely understood and adheres to the high standards of scholarly integrity that are expected in the scientific domain. It provides an example of how this latest approach can quickly identify novel therapeutic uses for well-known medications, advancing the field of drug discovery.