对DMPK的计算方法:现有方法及其实际影响的现实评估. 第一部分:物理化学和体外特性
Koichi Handa1, Mariko Hirano1, Michiharu Kageyama1
1Axcelead Tokyo West Partners, Inc., Translational Science Group, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-0065, Japan.
Drug discovery today
|July 2, 2025
概括
人工智能和计算模型越来越多地用于药物发现. 本综述评估了它们在预测药物代谢和药理动力学 (DMPK) 属性的实际用途.
科学领域:
- 计算化学和药理学计算化学和药理学
- 药物的发现和开发.
- 生物信息学和化学信息学
背景情况:
- 人工智能 (AI) 和计算方法在药物发现中越来越受欢迎.
- 评估这些in silico模型的实际实用性对于早期项目至关重要.
- 药物代谢和药理动力学 (DMPK) 属性是药物有效性和安全性的关键决定因素.
研究的目的:
- 审查和评估目前用于预测DMPK属性的in silico模型的实用性.
- 为了确定哪些计算模型适合用于早期药物发现的目的.
- 在药物开发中弥合计算方法和实验方法之间的差距.
主要方法:
- 对物理化学性质 (pKa,logD) 的近期in silico模型的审查.
- 在体外测定模型的评估:溶解性,透性,代谢稳定性和蛋白质结合.
- 分析数据生成,模型开发以及建模者与实验者之间的合作.
主要成果:
- 一些in silico模型显示出预测DMPK属性的前景,而另一些则需要进一步验证.
- 实用性因不同性质而异,一些物理化学和溶解性模型更为成熟.
- 计算和实验团队之间的有效互动对于成功实施模型至关重要.
结论:
- 人工智能和计算方法为加速药物发现提供了有价值的工具.
- 对于可靠的DMPK预测,需要仔细选择和验证in silico模型.
- 计算科学家和实验家之间的加强合作是最大化这些技术影响的关键.
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 ADMEDMPKK 是一个很好的方法.人工智能的人工智能是人工智能.发现药物的发现.登录D 登录D 登录D机器学习是机器学习.代谢稳定性 代谢稳定性在pKa pKa pKa透性 透性的与蛋白质结合的蛋白质结合溶解度 溶解度 溶解度 溶解度
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