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人工智能在抗体-药物联合开发中的作用

Yuxi Wang¹, Cuiyu Guo², Weimin Li¹

¹Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu, 610212, Sichuan, China.

Trends in pharmacological sciences

|November 11, 2025

概括

人工智能 (AI) 正在通过启用数据驱动工程来彻底改变抗体-药物联合体 (ADC) 开发. 人工智能加速ADC的设计和优化,以实现更有效的癌症治疗.

科学领域:

在瘤学瘤学.
生物技术是生物技术.
计算生物学计算生物学

背景情况:

抗体-药物合物 (ADC) 是针对癌症治疗的先进疗法.
ADC开发面临的挑战包括设计复杂性,毒性和可变的临床疗效.
优化ADC需要精确匹配抗体,链接器和有效载荷,以获得稳定性和瘤特异性释放.

研究的目的:

审查人工智能 (AI) 在推动抗体-药物联合体 (ADC) 开发中的应用.
要突出AI如何整合各种数据以改善ADC设计和临床翻译.
概述人工智能驱动下一代ADC的未来机会.

主要方法:

人工智能模型整合了抗体序列,结构和分子动力学 (MD) 特性.
数据驱动的方法正在将ADC开发从经验方法转向闭环工程.
综述综合了临床前和临床ADC开发阶段的进展.

主要成果:

人工智能加速ADC开发的关键方面,包括目标选择和并联优化.
人工智能模型增强了对ADC治疗患者反应的预测.
人工智能为ADC设计提供了更合理,更有效的方法.

结论:

人工智能正在将ADC开发转变为数据驱动,高效的过程.
人工智能集成有望克服ADC设计和临床应用当前的局限性.
人工智能支持的策略对于下一代抗体药物合物的未来至关重要.

关键词:

抗体药物联合体抗体药物联合体人工智能的人工智能是人工智能.发现药物的发现.机器学习是机器学习.

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