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通过微调的蛋白质语言模型提高临床前抗体的多活性预测

Yuwei Zhou¹, Haoxiang Tang¹, Changchun Wu¹

¹The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Journal of pharmaceutical analysis

|December 26, 2025

概括

早期预测抗体多活性是治疗单克隆抗体 (mAbs) 的关键. 一个新的工具,PolyXpert,准确地识别了多活性风险,改善了候选药物的选择.

科学领域:

生物技术是生物技术.
免疫学免疫学免疫学
计算生物学计算生物学

背景情况:

治疗性单克隆抗体 (mAbs) 对于疾病治疗至关重要.
在mAbs中高的多活性会导致异于目标的结合,降低疗效和批准率.
早期预测多重活性对于成功开发抗体至关重要.

研究的目的:

开发一个准确的计算工具来预测抗体多活性.
评估精调蛋白语言模型 (PLM) 对此预测任务的性能.
确定一个可靠的模型,用于早期选择治疗mAb候选人.

主要方法:

在抗体序列上微调六个预训练的蛋白质语言模型 (PLM).
使用诸如灵敏度,特异性,准确性,F1分数,MCC和AUC等指标评估模型性能.
将微调的语言模型分类器与那些在预先训练的模型嵌入上训练的分类器进行比较.

主要成果:

在独立测试数据集上,PolyXpert模型实现了高性能,灵敏度为90.10%,特异性为90.08%,准确度为90.10%.
在PolyXpert的研究中,AUC值为0.9672,这表明它具有强大的预测能力.
与基于嵌入的分类器相比,微调的语言模型分类器显示了增强的预测稳定性.

结论:

PolyXpert 作为一个有价值的体工具,用于评估抗体的多活性.
该工具可以帮助在临床开发中选择优质的治疗mAb候选者.
这种方法提高了早期抗体药物发现的可靠性.

关键词:

开发能力开发能力精细调整微调精细调整具有多重活性的多重活性.预测预测预测蛋白质语言模型的模型治疗性抗体治疗性抗体

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