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利用大型语言模型来预测抗体对流感A型血凝素的生物活性

Ella Barkan¹, Ibrahim Siddiqui², Kevin J Cheng³

¹IBM Research-Israel, Haifa, Israel.

Computational and structural biotechnology journal

|April 15, 2025

概括

人工智能 (AI) 模型可以预测抗体活性,加速药物发现. 这项研究开发了一种人工智能框架,用于预测抗体结合和阻断能力,简化新生物疗法的开发.

科学领域:

生物技术是生物技术.
计算生物学计算生物学
免疫学免疫学免疫学

背景情况:

单克隆抗体 (mAbs) 是FDA批准的各种疾病治疗的关键疗法.
传统的mAb发现是漫长而昂贵的.
机器学习 (ML) 和人工智能 (AI) 为加速mAb开发提供了潜在的解决方案.

研究的目的:

开发和评估一种人工智能模型,用于预测抗体结合和受体阻断活性对抗流感A型血凝素 (HA) 抗原.
仅使用序列信息来评估模型的性能.
探索AI在优先考虑实验室测试抗体候选人的实用性.

主要方法:

利用分子对齐多模式架构和语言 (MAMMAL) 框架进行生物学发现.
开发了一种AI模型,仅基于序列数据来预测抗体-抗原相互作用.
在各种数据分割条件下评估模型性能,以模拟现实应用.

主要成果:

获得了≥0.91的AUROC评分,用于预测对已知的HA抗原的活性.
获得了0.9的AUROC评分,用于预测对未见的HA抗原的活性.
新型抗体预测的AUROC得分为0.73,在严格的相似性约束下下降到0.63-0.66.

结论:

人工智能基础模型显示出革命性抗体设计的巨大潜力,并减少对广泛实验室测试的依赖.
开发的AI模型可以有效地优先考虑有前途的抗体候选人.
多样化和全面的抗体数据集对于提高预测模型的概括能力至关重要,特别是在新型抗体开发方面.

关键词:

人工智能 (AI) 是一种人工智能.具有约束力的测试.血液凝结抑制 (HAI) 测定方法血凝素 (HA) 是一种流感病毒是流感病毒.大型语言模型 (LLM)机器学习 (ML) 是指机器学习.单克隆抗体 (mAbs) 是一种单克隆抗体.

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