Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology
9.0
来源：
Nature
关键字：
computational pathology
发布时间：
2025-11-22 03:44
摘要：
AI is revolutionizing the development of antibody-drug conjugates (ADCs) by enhancing target identification, antibody engineering, and clinical applications. This review highlights how AI technologies streamline the ADC pipeline, from multi-omics integration for target discovery to optimizing antibody structures and predicting patient responses. The integration of AI facilitates personalized therapies, potentially improving clinical outcomes in oncology. Strategic priorities for future AI applications in ADC development are also discussed, emphasizing the need for curated datasets and interpretable models.
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domain_focus
1.0分+核心领域符合度
business_impact
1.0分+提及商业合作和临床应用
scientific_rigor
1.5分+有具体实验数据和研究支持
timeliness_innovation
1.5分+包含突破性技术和创新
investment_perspective
2.5分+处于早期研发阶段
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1.0分+涉及高发疾病
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0.5分+知名机构背景
technical_barrier_competition
1.0分+技术壁垒高
关键证据
AI empowers each stage of the ADC pipeline.
AI facilitates patient stratification, response prediction, and trial simulation.
The review outlines strategic priorities for AI integration in ADC development.
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AI评分总结
AI is revolutionizing the development of antibody-drug conjugates (ADCs) by enhancing target identification, antibody engineering, and clinical applications. This review highlights how AI technologies streamline the ADC pipeline, from multi-omics integration for target discovery to optimizing antibody structures and predicting patient responses. The integration of AI facilitates personalized therapies, potentially improving clinical outcomes in oncology. Strategic priorities for future AI applications in ADC development are also discussed, emphasizing the need for curated datasets and interpretable models.