转移学习能够对小样本进行准确的药理动力学预测
Wenbo Guo1, Yawen Dong2, Ge-Fei Hao1
1National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China.
转移学习是一种机器学习技术,使用有限的数据准确预测药理动力学 (PK) 特性. 本综述探讨了它对药物设计研究人员的应用和挑战.
科学领域:
- 药理动力学 药理动力学
- 机器学习 机器学习
- 药物发现 药物发现 药物发现
背景情况:
- 准确的药理动力学 (PK) 评估对于选择候选药物和预防晚期失败至关重要.
- 转移学习 (TL) 提供了一种强大的机器学习方法,用于高通量预测,特别是在有限的数据集.
- 最近的进展突显了TL在预测ADME/PK参数方面的潜力,引发了大量的研究兴趣.
研究的目的:
- 为PK预测提供转移学习技术的全面审查.
- 探索TL在PK中的基本原理,分类,工具包和应用.
- 作为药物设计研究人员利用TL进行PK分析的宝贵参考资料.
主要方法:
- 应用到PK预测的转移学习方法的文献综述.
- 不同TL方法的分类与PK相关.
- 通过三个实践案例研究来证明TL的实用性.
主要成果:
- 转移学习方法在高通量PK预测方面显示出显著的前景.
- 各种TL工具包和技术适用于PK参数估计.
- 案例研究说明了TL在药物设计中的实际实用性和好处.
结论:
- 转移学习是准确评估PK属性的有效策略.
- 了解TL的优势和挑战对于其在药物发现中的成功实施至关重要.
- 本综述为研究人员在PK研究中利用TL提供了基础资源.
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