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<p>This review will examine how artificial intelligence, profound learning technologies, has affected drug discovery. Deep learning&#xa0;technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. Integrating clinical trial data, target identification or lead optimization, and personalized medicine have all become possible thanks to DLT. Given the explosion in IUPAC-compliant compounds registered with PubChem or derived from existing ones, DLT has given the pharmaceutical industry a massive booster shot. We will explore the key role generative models play in creating new drug compounds and why interdisciplinary collaboration is essential to entirely using AI's potential for drug discovery. In addition, the purpose of this article is to consider further perspectives concerning what problems exist at present in deep learning and AI-driven drug discovery. We focus on its potential as an accelerated, more effectiveeven tailored healthcare technology. As AI technology advances, a new field emerges in drug development, tipping the global balance between 'well' and 'ill.'</p>

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Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月23日

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  • Nouman Ali,
  • Nimra Hanif,
  • Hassan Abbas Khan,
  • Muhammad Abdullah Waseem,
  • Afshan Saeed,
  • Sadia Zakir,
  • Abeeha Khan,
  • Mejerrah Aamir,
  • Adeeba Ali,
  • Aamir Ali,
  • Amna Saleem

摘要

This review will examine how artificial intelligence, profound learning technologies, has affected drug discovery. Deep learning technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. Integrating clinical trial data, target identification or lead optimization, and personalized medicine have all become possible thanks to DLT. Given the explosion in IUPAC-compliant compounds registered with PubChem or derived from existing ones, DLT has given the pharmaceutical industry a massive booster shot. We will explore the key role generative models play in creating new drug compounds and why interdisciplinary collaboration is essential to entirely using AI's potential for drug discovery. In addition, the purpose of this article is to consider further perspectives concerning what problems exist at present in deep learning and AI-driven drug discovery. We focus on its potential as an accelerated, more effectiveeven tailored healthcare technology. As AI technology advances, a new field emerges in drug development, tipping the global balance between 'well' and 'ill.'