一种机器学习支持的定量结构-活动关系模型,用于预测狗中药物的血半衰期
Xue Wu1,2,3, Pei-Yu Wu1,2,3, Wei-Chun Chou4
1Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, 2187 Mowry Road, Gainesville, Florida, 32611, USA.
预测狗的药物血半衰期对于狗的发育至关重要. 基于化学结构的机器学习模型可以准确预测药物消除半衰期,帮助犬类药物开发和物种间推断.
科学领域:
- 药理动力学和药物开发
- 计算化学计算化学
- 兽医药理学 兽医药理学
背景情况:
- 准确预测药物的血半衰期对于优化药物开发中的剂量方案至关重要.
- 药物动力学数据,特别是血半衰期,对于优化治疗结果至关重要.
- 预测药物半衰期的现有方法可能耗时且资源密集.
研究的目的:
- 开发基于机器学习的定量结构-活动关系 (QSAR) 模型,用于预测狗类药物清除半衰期.
- 利用化学描述符和监督机器学习算法来构建预测模型.
- 建立一个支持兽医早期药物开发的计算工具.
主要方法:
- 从食品动物残留物避免数据库收集了560个用于狗的药物的血半衰期数据点.
- 预处理的药理动力学数据,为模型训练选择平均消除半衰期.
- 采用五种类型的化学描述符和四种监督机器学习算法来构建QSAR模型.
主要成果:
- 深度神经网络模型,利用所有结合的描述符类型,显示出最佳性能.
- 在五倍交叉验证组中达到0.80的R平方值,在测试组中达到0.57的R平方值.
- 训练模型的适用性领域使用威廉姆斯图形可视化.
结论:
- 开发了一种有效的基于ML的QSAR工具,用于从化学结构中预测犬类药物消除半衰期.
- 这种预测工具可以显著帮助狗的药物开发过程.
- 这项研究为潜在的药物药理动力学特性跨物种推断提供了基础.
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