人工神经网络
堆积
贝叶斯网络
贝叶斯概率
计算机科学
财产(哲学)
人工智能
贝叶斯推理
药代动力学
贝叶斯分层建模
机器学习
生物系统
化学
药理学
生物
哲学
有机化学
认识论
作者
Yuanyuan Zhang,Zhiyin Xie,Xiao Fu,Jie Yu,Zhehuan Fan,Shihui Sun,Jiangshan Shi,Zunyun Fu,Xutong Li,Dingyan Wang,Mingyue Zheng,Xiaomin Luo
标识
DOI:10.1021/acs.molpharmaceut.4c00406
摘要
Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive data set comprising parameters across multiple species. Building upon this groundwork, we introduced the PKStack ensemble model to predict PK parameters across diverse species. PKStack integrates a variety of base models and includes uncertainty in its predictions. We also manually collected PK data from animals as an external test set. We predicted a total of 45 tasks for nine PK parameters in five species, and in general, the prediction accuracy was better for intravenous injections, including parameters such as human
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