多任务学习用于预测化学品的肺吸收
Yu-Wen Chiu1, Chun-Wei Tung2, Chia-Chi Wang1
1Department and Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei, 106, Taiwan.
这项研究开发了一种机器学习模型,使用隔离透气肺 (IPL) 数据来预测肺部的化学吸收. 该模型准确地估计了肺吸收率,有助于减少用于药物和有毒物质评估的动物试验.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算化学的计算化学
- 在模拟中
背景情况:
- 肺吸收对于药物输送和化学物质暴露评估至关重要.
- 使用细胞培养和计算方法等无动物模型,但ex vivo隔离透气肺 (IPL) 模型具有更高的临床相关性.
- 目前的IPL实验资源密集,限制了吸入物质的大规模查.
研究的目的:
- 开发一种新的机器学习 (ML) 方法,用于预测隔离透气肺 (IPL) 中的吸收速率常数 (ka_IPL).
- 为了利用多任务学习,从Caco-2和Calu-3细胞透性数据中提取共享的透性知识,以及IPL吸收.
- 确定影响肺吸收的关键物理化学描述剂.
主要方法:
- 基于超级任务的多任务学习方法被用来预测ka_IPL.
- 同时学习三个任务:Caco-2细胞透性,Calu-3细胞透性和IPL吸收率.
- 识别和使用七个信息性的物理化学描述符.
主要成果:
- 开发的ML模型表现出良好的预测性能.
- 在一个独立的测试数据集中,在预测和观察的ka_IPL值之间观察到高相关性 (r = 0.84).
- 七个关键的物理化学描述因素被确定为肺吸收预测的信息.
结论:
- 这种新型的多任务学习模型有效地使用IPL数据预测肺吸收率.
- 该模型显示了作为选吸入药物和有毒物质的有价值工具的潜力,支持减少动物试验.
- 关于吸入药物和呼吸系统敏化剂的案例研究突出显示了 in silico 方法的实际可用性.
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