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Background <p>The growing global health burden necessitates precise therapeutics to mitigate pathogenesis and severe complications, which are increasing daily. Recently emerging and re-emerging viral infectious diseases, along with other ongoing complications from diverse infections and pathogens, contribute to global outbreaks. To address this, immediate and accurate therapeutic developments that can help manage this crisis are needed. Concerning the development of therapeutics, the conventional method-based drug design is time-consuming and requires a substantial investment of time to develop a drug against the pathogen successfully.</p> Main body of the abstract <p>To overcome these present obstacles, artificial intelligence (AI) came as a hope of revolutionizing the detection and advancement of pioneering, precise, cost- and time-effective drugs. AI uses advanced algorithms to improve the accuracy regarding target identification and further inhibitor selection. The pathogens were re-emerging daily, simultaneously, generating a huge amount of data with various specific properties and other essential details. Among them, some data can be helpful for therapeutic development. Using AI-based pipelines, tools, servers, databases, and useful resources to aid drug discovery, and employing different algorithms to examine the data, it was possible to identify a potential target that could aid therapeutic development; similarly, it also helped revolutionize the clinical aspects of drug discovery and the pharmaceutical industry by enabling more specific data handling. Moreover, it can help utilize available drugs and their significant details to address emerging and ongoing diseases through a drug repurposing-based approach using advanced AI-based computational analysis.</p> Short conclusion <p>Herein, this study offers the AI insight toward the drug discovery and development, how these approaches were utilized, and their advancements and challenges.</p>

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Navigation of drug discovery via artificial intelligence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月9日

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  • Saurav Kumar Mishra,
  • J Jeba Praba,
  • Hamadou Mamoudou,
  • Akansha Subba,
  • John J. Georrge

摘要

Background

The growing global health burden necessitates precise therapeutics to mitigate pathogenesis and severe complications, which are increasing daily. Recently emerging and re-emerging viral infectious diseases, along with other ongoing complications from diverse infections and pathogens, contribute to global outbreaks. To address this, immediate and accurate therapeutic developments that can help manage this crisis are needed. Concerning the development of therapeutics, the conventional method-based drug design is time-consuming and requires a substantial investment of time to develop a drug against the pathogen successfully.

Main body of the abstract

To overcome these present obstacles, artificial intelligence (AI) came as a hope of revolutionizing the detection and advancement of pioneering, precise, cost- and time-effective drugs. AI uses advanced algorithms to improve the accuracy regarding target identification and further inhibitor selection. The pathogens were re-emerging daily, simultaneously, generating a huge amount of data with various specific properties and other essential details. Among them, some data can be helpful for therapeutic development. Using AI-based pipelines, tools, servers, databases, and useful resources to aid drug discovery, and employing different algorithms to examine the data, it was possible to identify a potential target that could aid therapeutic development; similarly, it also helped revolutionize the clinical aspects of drug discovery and the pharmaceutical industry by enabling more specific data handling. Moreover, it can help utilize available drugs and their significant details to address emerging and ongoing diseases through a drug repurposing-based approach using advanced AI-based computational analysis.

Short conclusion

Herein, this study offers the AI insight toward the drug discovery and development, how these approaches were utilized, and their advancements and challenges.