药物发现中的代谢物识别数据,第二部分:代谢部位的注释,分析和探索用于机器学习
Ya Chen1,2, Susanne Winiwarter3, Roxane Axel Jacob1,4
1Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.
Molecular pharmaceutics
|October 21, 2025
概括
预测代谢部位 (SoM) 对于药物设计至关重要. 这项研究引入了一种新方法来注释SoM数据,改善机器学习预测并发布有价值的公共数据.
科学领域:
- 药物的发现和开发.
- 药品化学 药品化学 是一个
- 药理动力学 药理动力学
背景情况:
- 准确预测代谢部位 (SoM) 对于设计安全有效的小分子至关重要.
- 标注SoM数据的有限可用性阻碍了数据驱动预测方法的进展,包括机器学习.
- 现有的SoM数据集往往缺乏全面的表征,并且无法充分考虑实验数据的不确定性.
研究的目的:
- 从人类肝细胞测定中全面描述SoM数据.
- 开发和验证一个新的SoM注释策略,该策略包含实验不确定性.
- 使用新生成和分析的新陈代谢数据,提高SoM预测模型的准确性.
主要方法:
- 从人类肝细胞测定中获取和描述SoM数据.
- 开发一个新的SoM注释策略来解决数据不确定性.
- 对SoM注释的透分析,以了解数据的复杂性.
- 评估新数据对SoM预测模型性能的影响.
主要成果:
- 一个大量的SoM注释数据集被生成和特征.
- 新的注释策略有效地处理实验代谢数据中的不确定性.
- 积分析揭示了解释新陈代谢数据的复杂性.
- 新获得的数据显著提高了SoM预测模型的性能.
结论:
- 开发的SoM注释策略提高了用于计算建模的新陈代谢数据的可靠性.
- 这个全面的SoM数据集的公开发布将加速基于机器学习的代谢预测的进步.
- 这项工作为药物发现研究人员提供了宝贵的资源,旨在优化小分子特性.
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