在药物设计阶段对人类药物动力学的可操作预测
Leonid Komissarov1, Nenad Manevski1, Katrin Groebke Zbinden1
1Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel 4070, Switzerland.
Molecular pharmaceutics
|August 12, 2024
概括
这项研究引入了一种新的计算方法,用于在大型数据集上使用机器学习来预测人类药理动力学 (PK). 该方法提供了准确的预测和不确定性估计,有助于早期药物设计.
科学领域:
- 计算化学是一种计算化学.
- 药理动力学 药理动力学
- 机器学习在药物发现中的作用
背景情况:
- 药物设计的早期阶段在预测人类的药理动力学 (PK) 方面面临着挑战.
- 准确的PK预测对于有效的药物开发和减少对临床前研究的依赖至关重要.
研究的目的:
- 开发和验证一种新的计算方法来预测人类的药理动力学 (PK).
- 为机器学习模型培训创建一个临床PK终点的大规模数据集.
- 在药物设计过程的早期提供可操作的PK预测.
主要方法:
- 开发了一种机器学习模型,该模型基于超过2700个化学结构和11个临床PK终点的数据集进行训练.
- 高级培训策略的比较,包括体外数据集成和自我监督的预培训.
- 将认识不确定性量化纳入预测模型.
主要成果:
- 该模型在多个 PK 终点上实现了不到 2.5 的绝对平均折叠误差 (AAFE).
- 在特定地区表现出卓越的预测性能.
- 成功为所有预测提供了有意义的认识体系不确定性.
结论:
- 这种新的计算方法为早期药物设计的可操作的PK预测提供了显著的进步.
- 该方法可以通过减少对广泛非临床研究的需求来加速药物开发.
- 大数据集和先进的机器学习技术的整合提高了预测准确性和可靠性.
关键词:
在PK PK PK中,PK是PK.临床临床临床临床临床临床数据集数据集数据集图形神经网络的神经网络人类 人类 人类 人类 人类 人类 人类机器学习是机器学习.药物动力学 药物动力学预测 预测 预测 预测转移学习转移学习不确定性量化不确定性量化更多相关视频
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