PROPERMAB:使用机器学习预测抗体开发能力的综合框架
Bian Li1, Shukun Luo2, Wenhua Wang2
1Therapeutic Proteins, Regeneron Pharmaceuticals, Inc, Tarrytown, NY, USA.
mAbs
|March 5, 2025
概括
使用计算模型对抗体开发能力的早期评估可以加速药物开发. PROPERMAB (单克隆抗体的特性) 可以从序列中预测关键的生物物理特性,节省时间和资源.
科学领域:
- 生物制药的发展.
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
背景情况:
- 治疗分子的选择优先考虑有效性和安全性,而可开发性是迟迟评估的.
- 晚期开发能力评估延迟了药物开发,增加了成本.
- 对抗体开发能力的早期评估对于有效的药物发现至关重要.
研究的目的:
- 为早期大规模预测单克隆抗体可开发性质开发一个计算框架.
- 通过使用机器学习,使抗体开发能力的成本效益和高通量评估成为可能.
- 为了加速抗体治疗的发现到临床的时间表.
主要方法:
- 开发了PROPERMAB (单克隆抗体的特性),这是一个用于in silico预测的计算框架.
- 利用定制分子特征和机器学习建模来进行属性预测.
- 预先训练的模型可以直接从抗体序列中预测结构衍生的特征.
主要成果:
- 成功开发了模型来预测抗体疏水性相互作用染色体保留时间和高度粘度.
- 从序列中证明了结构衍生特征的快速和准确预测.
- 展示了对目录规模序列数据集的方法的可扩展性.
结论:
- PROPERMAB 能够有效地预测抗体可开发性质的在体内预测.
- 早期预测使用序列衍生特征的可开发性加速了生物制药研究.
- 计算方法为抗体开发能力评估提供了实验方法的强大替代方案.
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