药物发现中的代谢物识别数据,第1部分:数据生成和趋势分析
Marie Ahlqvist1, Isabella Bonner Karlsson1, Anja Ekdahl1
1Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, 431 83 Mölndal, Sweden.
Molecular pharmaceutics
|October 14, 2025
概括
药物发现利用代谢物识别数据来确定分子中的代谢负债. 这项研究与AstraZeneca相同.
科学领域:
- 药物发现和开发 药物发现和开发
- 代谢学和化学信息学
背景情况:
- 代谢物识别数据在药物发现中至关重要,用于优化分子设计和评估安全性.
- 了解代谢物结构有助于识别活性,反应性或有毒代谢物的潜在风险.
研究的目的:
- 为了展示代谢物识别数据是如何在AstraZeneca的药物发现计划中生成和应用的.
- 为120种化合物共享来自人类肝细胞化过程中的代谢物转化方案数据集.
- 探索这些数据对基于机器学习的药物代谢预测的有用性.
主要方法:
- 从体外人类肝细胞化中生成代谢物转化方案数据的生成.
- 化学空间分析和体外代谢物识别数据的比较.
- 观察到的代谢途径的表征和体外与体外数据的相关性.
主要成果:
- 介绍了120种化合物的代谢物转化方案的数据集.
- 分析包括化学空间比较和代谢途径的表征.
- 对于选定的化合物,体外和体内代谢物数据之间的相关性已被证明.
结论:
- 代谢物识别数据对于指导药物设计和安全性评估是有价值的.
- 可以利用共享的数据来利用AI/ML进行先进的药物代谢预测.
- 这是一个以数据为导向的方法,以提高药物发现效率和安全的例子.
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