DeepCt:使用深度学习从化学结构预测药物动力学度-时间曲线和分区模型
Maximilian Beckers1, Dimitar Yonchev1, Sandrine Desrayaud1
1Biomedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland.
DeepCt是一种新的深度学习方法,可以从分子结构中预测药物度-时间概况. 这种方法通过估计药理动力学参数和减少动物试验,有助于早期药物开发.
科学领域:
- 药理学和药物开发领域
- 计算化学计算化学
- 机器学习在生命科学中的应用
背景情况:
- 在临床前药物开发中的药物动力学 (PK) 研究评估了哺乳动物随时间推移的药物度.
- 度-时间 (C-t) 档案对于导出PK参数至关重要,指导分子选择.
- 目前的机器学习工作往往预测PK参数,而不是C-t配置文件本身,限制了机械洞察力.
研究的目的:
- 介绍DeepCt,一种新的深度学习方法,用于直接从复合结构中预测C-t配置文件.
- 为了能够预测底层的机理性药理动力学模型.
- 为了促进单剂量和多剂量CT特征的模拟和预测.
主要方法:
- 开发一个深度学习模型 (DeepCt) 用于C-t概况预测.
- 整合机械分区药理动力学建模原理.
- 从化学化合物结构中预测C-t曲线的应用.
主要成果:
- DeepCt成功地从复合结构中预测了C-t形状.
- 该方法允许预测潜在的机械 PK 模型.
- 能够对各种剂量场景进行模拟和预测.
结论:
- DeepCt提供了一种新的深度学习解决方案,用于预测药物C-t概况.
- 这种方法可以通过改善PK预测和减少动物研究来增强早期药物发现.
- 机械模型方面为ADME流程提供了更深入的见解.
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