预测药物度的机器学习:应用和挑战
Shuqi Huang1,2, Qihan Xu1, Guoping Yang1,3
1Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.
机器学习模型在药理动力学中预测药物度方面表现有希望,以树为基础的算法和神经网络是最常见的. 集合方法进一步提高了预测的准确性.
科学领域:
- 药理动力学 药理动力学
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 由于算法进步和数据可用性,机器学习 (ML) 越来越多地应用于药理动力学 (PK) 分析.
- 药物动力学研究从ML中受益,用于预测药物度和了解药物处置.
研究的目的:
- 在2024年9月之前审查机器学习在药理动力学中的应用.
- 总结ML算法,数据预处理,应用场景和PK分析中的挑战.
- 评估ML模型的性能与传统的人口药理动力学 (PopPK) 模型相比.
主要方法:
- 在PubMed和IEEE Xplore数据库中的系统文献搜索.
- 对研究的分析,重点是药物度在药理动力学中的预测的ML.
- 机器学习算法的分类,数据预处理技术和应用领域.
主要成果:
- 基于树的算法和神经网络是PK中最常用的ML方法.
- ML模型的性能与传统的PopPK模型相美.
- 整体建模,特别是结合ML和药理学,可以提高预测的准确性.
结论:
- 机器学习为药理动力学分析提供了强大的替代方案,特别是用于药物度预测.
- 对ML算法和组合技术的持续研究对于推进PK建模至关重要.
- 解决当前的挑战将进一步将ML纳入常规药理动力学评估中.
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