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<p>Generative artificial intelligence is reshaping how researchers discover protein-binding compounds and develop them into drug candidates. Unlike traditional methods that screen existing molecules, structure-based generative AI designs novel compounds tailored to a protein’s three-dimensional binding pocket. This review outlines how these approaches are applied in early drug discovery, focusing on general principles. We categorize methods according to their generative modeling paradigms and their strategies for using structural data to guide molecular design, distinguishing de novo incremental builders from models that generate full structures. We also survey lead-optimization techniques, highlighting a recent shift toward generation-driven medicinal chemistry.</p>

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Generative AI in structure-based drug discovery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月21日

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  • Zhuoya Zhong,
  • Jacob D. Durrant

摘要

Generative artificial intelligence is reshaping how researchers discover protein-binding compounds and develop them into drug candidates. Unlike traditional methods that screen existing molecules, structure-based generative AI designs novel compounds tailored to a protein’s three-dimensional binding pocket. This review outlines how these approaches are applied in early drug discovery, focusing on general principles. We categorize methods according to their generative modeling paradigms and their strategies for using structural data to guide molecular design, distinguishing de novo incremental builders from models that generate full structures. We also survey lead-optimization techniques, highlighting a recent shift toward generation-driven medicinal chemistry.