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<p>Artificial intelligence (AI) is opening new frontiers in the development of antibody-drug conjugates (ADCs), offering unprecedented opportunities for precision therapy. This review outlines how AI empowers each stage of the ADC pipeline. In target discovery, multi-omics integration and graph-based learning prioritize tumor-selective and internalizing antigens. In antibody engineering, structure prediction, affinity optimization, and developability modeling streamline candidate selection. For linker-payload design, generative models and multi-objective optimization approaches support the rational design of conjugates that balance potency, stability, and immunogenicity. In absorption, distribution, metabolism, excretion, and toxicity (ADMET) modeling, deep learning and transformer-based frameworks predict pharmacokinetics and toxicity with increasing accuracy and mechanistic clarity. In clinical development, AI facilitates patient stratification, response prediction, and trial simulation through digital twin models, adaptive dosing algorithms, and real-world data integration. These capabilities support a more personalized and efficient pathway from bench to bedside. To further realize the impact of AI in ADC development, we highlight strategic priorities including the creation of curated, multimodal datasets, interpretable model architectures, and closed-loop experimental platforms. Together, these advances will be essential for realizing the full potential of AI to support rational, scalable, and personalized ADC-based therapies in oncology.</p>

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Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Ye Lu,
  • Weijun Huang,
  • Yuxuan Li,
  • Yanzhi Xu,
  • Qing Wei,
  • Chulin Sha,
  • Peng Guo

摘要

Artificial intelligence (AI) is opening new frontiers in the development of antibody-drug conjugates (ADCs), offering unprecedented opportunities for precision therapy. This review outlines how AI empowers each stage of the ADC pipeline. In target discovery, multi-omics integration and graph-based learning prioritize tumor-selective and internalizing antigens. In antibody engineering, structure prediction, affinity optimization, and developability modeling streamline candidate selection. For linker-payload design, generative models and multi-objective optimization approaches support the rational design of conjugates that balance potency, stability, and immunogenicity. In absorption, distribution, metabolism, excretion, and toxicity (ADMET) modeling, deep learning and transformer-based frameworks predict pharmacokinetics and toxicity with increasing accuracy and mechanistic clarity. In clinical development, AI facilitates patient stratification, response prediction, and trial simulation through digital twin models, adaptive dosing algorithms, and real-world data integration. These capabilities support a more personalized and efficient pathway from bench to bedside. To further realize the impact of AI in ADC development, we highlight strategic priorities including the creation of curated, multimodal datasets, interpretable model architectures, and closed-loop experimental platforms. Together, these advances will be essential for realizing the full potential of AI to support rational, scalable, and personalized ADC-based therapies in oncology.