基于机器学习的定量结构-活性关系模型的开发,用于预测六种常见的食品动物物种中药物的血半衰期
Pei-Yu Wu1,2, Wei-Chun Chou1,2, Xue Wu1,2
1Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, United States.
这项研究开发了一种机器学习模型,用于预测食品动物的药物血半衰期,这对于确定安全的戒断间隔至关重要. 深度神经网络模型准确地预测了半衰期,提高了食品安全.
科学领域:
- 药理动力学 药理动力学
- 兽医药理学 兽医药理学
- 计算化学计算化学
背景情况:
- 血半衰期对于在生产食品的动物中建立标签外药物戒断间隔至关重要.
- 确保食品安全需要准确的药理动力学参数估计.
研究的目的:
- 开发一种定量结构-活性关系 (QSAR) 模型,用于预测六种食物动物物种的药物血半衰期.
- 整合机器学习和人工智能,以提高预测准确度.
主要方法:
- 通过将4个机器学习算法与5种分子描述器类型相结合,开发了20个QSAR模型.
- 使用了来自食品动物残留物避免数据库 (FARAD) 的比较药物动力学数据库的数据.
- 确定了深度神经网络 (DNN) 算法作为表现最好的.
主要成果:
- 纳入所有描述符的DNN模型在训练组中实现了0.82 ± 0.19的高确定系数 (R2),在独立测试组中达到0.67.
- 证明了药物等离子体半衰期的准确预测和良好的概括性.
- 该模型被部署为一个用户友好的网络仪表板.
结论:
- 开发的基于机器学习的QSAR模型是预测药物的血半衰期和食品动物的标签外戒断间隔的宝贵工具.
- 为预测食品动物中药物组织半衰期的未来模型提供了基础.
- 促进在科学研究和监管实践中更广泛的应用.
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