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<p>The rapid evolution of molecular representation methods has significantly advanced the drug discovery process. Advances in language models, graph-based representations, and novel learning strategies have greatly improved the ability to characterize molecules. These AI-driven strategies extend beyond traditional structural data, facilitating exploration of broader chemical spaces and accelerating scaffold hopping. This review summarizes key advancements, discusses their advantages over conventional techniques, and highlights challenges in data quality and real-world applications.</p>

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Recent advances in molecular representation methods and their applications in scaffold hopping

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月28日

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  • Shihang Wang,
  • Ran Zhang,
  • Xiangcheng Li,
  • Fengyu Cai,
  • Xinyue Ma,
  • Yilin Tang,
  • Chao Xu,
  • Lin Wang,
  • Pengxuan Ren,
  • Lu Liu,
  • Sanan Wu,
  • Qiyang Qian,
  • Fang Bai

摘要

The rapid evolution of molecular representation methods has significantly advanced the drug discovery process. Advances in language models, graph-based representations, and novel learning strategies have greatly improved the ability to characterize molecules. These AI-driven strategies extend beyond traditional structural data, facilitating exploration of broader chemical spaces and accelerating scaffold hopping. This review summarizes key advancements, discusses their advantages over conventional techniques, and highlights challenges in data quality and real-world applications.