机器学习模型的应用用于对向蛋白质降解剂的属性预测
Giulia Peteani1, Minh Tam Davide Huynh1, Grégori Gerebtzoff1
1Novartis Biomedical Research, Novartis Campus, 4002, Basel, Switzerland.
Nature communications
|July 9, 2024
概括
机器学习模型有效地预测了向蛋白质降解剂 (TPD) 的特性,与其他药物模式相比. 这支持在TPD设计中使用机器学习来加速药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 药物发现 药物发现
背景情况:
- 机器学习 (ML) 模型使用定量结构-属性关系 (QSPR) 预测分子性质.
- ML和QSPR模型对向蛋白降解剂 (TPD) 的应用尚未得到充分证实.
- 在以TPD为中心的药物发现项目中,ML的使用仍然有限.
研究的目的:
- 开发和评估ML模型,以预测TPD的吸收,分布,新陈代谢和分泌 (ADME) 和物理化学性质.
- 评估ML模型在TPD子模式上的性能,如分子和异构生物功能物.
- 调查转移学习策略,以改善TPD设计中的ML预测.
主要方法:
- 开发和评估基于ML的QSPR模型,用于TPD属性预测.
- 对被动透性,代谢清除,CYP450抑制,血蛋白结合和脂性等预测错误的分析.
- 在TPD子模式 (粘合剂,异构功能) 和其他药物模式中对模型性能进行比较.
主要成果:
- 在TPD上ML模型的性能与其他药物模式相美.
- 分子和异位生物功能物分别显示出较低和较高的预测误差.
- 对关键ADME属性的错误分类错误很低 (例如,粘合剂<4%,异构生物功能的<15%).
- 转移学习策略增强了对异性双功能TPD的预测.
结论:
- 基于ML的QSPR模型适用于TPD,包括分子和异构生物功能物.
- 这些发现支持在TPD设计中扩大ML的使用,以加速药物发现.
- 这项研究为TPD ADME和物理化学性质预测提供了ML的首次全面评估.
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