机器学习驱动的生物制药配方开发加速使用辅助剂预测软件 (ExPreSo)
Estefania Vidal-Henriquez1, Thomas Holder1, Nicholas Franciss Lee1
1LEUKOCARE AG, Am Klopferspitz 19a, 82152 Martinsried, Munich, Germany.
Computational and structural biotechnology journal
|November 11, 2025
概括
辅助剂预测软件 (ExPreSo) 是一款新的机器学习工具,用于建议蛋白质生物制药的最佳辅助剂. 这种计算方法通过基于蛋白质特性预测辅助剂来帮助配方开发,减少实验选.
科学领域:
- 生物制药配方 生物制药配方
- 计算化学的计算化学
- 机器学习在药物开发中的作用
背景情况:
- 蛋白质生物制药配方是复杂的,面临着新药模式和高度的挑战.
- 有限的药物成分和广泛的分析需求阻碍了实证辅助剂查.
- 在实验室工作之前,急需使用in silico工具来指导辅助剂的选择.
研究的目的:
- 引入辅助剂预测软件 (ExPreSo),这是一个用于预测蛋白质生物制药中的辅助剂的机器学习算法.
- 为了利用蛋白质特性和目标产品概况进行in silico辅助剂预选.
- 为了减少与传统辅助剂选方法相关的时间,成本和风险.
主要方法:
- 开发了ExPreSo,这是一个受监督的机器学习算法,在335个监管部门批准的和蛋白质药物产品上进行了训练.
- 利用预测特征,包括蛋白质结构性质,语言模型嵌入和药物产品特征.
- 评估了各种ExPreSo变体的性能,包括基于序列和仅蛋白质的特征集.
主要成果:
- 对于9种常见的辅助剂,ExPreSo表现出了良好的预测性能,并具有最小的过拟合.
- 一个快速的,基于序列的ExPreSo变体实现了与较慢的,基于分子建模的版本相比的预测能力.
- 仅使用蛋白质特征的ExPreSo变体显示出强大的性能,不受平台配方影响.
结论:
- ExPreSo是第一个机器学习算法,可以使用已批准的药物产品数据集来建议生物制药辅助剂.
- 该软件显示出在生物制药配方开发中简化辅助剂选的巨大潜力.
- ExPreSo可以减少基于蛋白质的治疗方法的实验工作量,成本和开发时间表.
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