从开放的ADME信息中有效处理膜透性数据的新工作流
Tsuyoshi Esaki1,2, Tomoki Yonezawa3, Kazuyoshi Ikeda3,4
1Faculty of Data Science, Shiga University, 1-1-1 Banba, Hikone, Shiga, 522-8522, Japan. tsuyoshi-esaki@biwako.shiga-u.ac.jp.
Journal of cheminformatics
|March 14, 2024
概括
这项研究引入了一种自动化工作流程,用于在MDCK,LLC-PK1和RRCK等细胞系的体外膜通透性数据的策划. 这确保了高质量的数据,用于开发药物发现中精确的in silico药理动力学模型.
科学领域:
- 药理动力学和药物新陈代谢
- 计算化学和化学信息学
- 在中发现药物发现
背景情况:
- 膜透性 (表面透性,Papp) 对于药物开发至关重要,影响吸收,分布,新陈代谢和分泌 (ADME).
- 虽然Caco-2细胞很常见,但其他细胞系,如Madin-Darby犬 (MDCK),LLC-猪1 (LLC-PK1) 和Ralph Russ犬 (RRCK) 也用于帕普估计.
- 高质量的Papp体外数据对于开发可靠的in silico模型至关重要,特别是随着人工智能和深度学习的兴起,在预测药物动力学特性方面.
研究的目的:
- 开发一种自动化工作流程,用于在MDCK,LLC-PK1和RRCK细胞系中的Papp实验室数据的策划.
- 确保数据质量和准确性,以培训强大的药物发现的模型.
- 提供可重复使用和可更新的工具,加速预测性药理动力学模型的开发.
主要方法:
- 使用KNIME软件构建自动数据整理工作流程.
- 从ChEMBL数据库中提取Papp数据,重点关注MDCK,LLC-PK1和RRCK细胞系的特定协议.
- 从436篇文章中过和质量检查数据,产生1661个高质量的条目.
主要成果:
- 成功开发并实施自动化数据策划工作流程.
- 从广泛的文献中识别并保留了1661个高质量的Papp数据条目.
- 工作流是自由可用的,设计用于更新,并且高度可重复使用.
结论:
- 开发的工作流显著提高了Papp体外数据集的质量和可靠性.
- 自动化数据策划可以减少与构建准确的in silico预测模型相关的时间和成本.
- 这种新的方法有助于开发有效的in silico模型,加速药物发现工作.
关键词:
ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME切姆布尔 (Chembl) 是一个数据策划数据的策划.在形模型模型.在KNIME工作流程中.膜的透性 膜的透性更多相关视频
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