Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.
Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.
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使用预测的组织使用基于决策树的回归方法来预测分布的体积：等离子体分配系数。
DOI：
10.1186/s13321-015-0054-x
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发表时间：
2015
影响因子：
8.6
通讯作者：
Ghafourian T
中科院分区：
文献类型：
--
作者：
Freitas AA;Limbu K;Ghafourian T
分布容积是一种重要的药代动力学性质，它表示药物在人体组织中的分布程度。本文讨论的问题是如何估计表观分布体积在稳态（Vss）的化合物在人体内使用基于决策树的回归方法从数据挖掘（或机器学习）的区域。因此，已经讨论了几种不同类型的基于决策树的回归方法的优点和缺点。回归方法使用化合物的分子描述符和化合物的组织：血浆分配系数（Kt：p）（通常用于基于生理学的药代动力学）作为预测特征来预测Vss。因此，这项工作已经评估了是否可以通过使用不仅是化合物的分子描述符而且是其预测的Kt：p值（的子集）作为输入来使Vss的基于数据挖掘的预测更准确。将仅使用分子描述符的模型（特别是Bagging决策树（平均倍数误差为2.33））与除分子描述符之外还使用预测的Kt：p值的模型（例如使用脂肪Kt：p的Bagging决策树（平均倍数误差为2.29））进行比较，表明如果应用先前特征选择，则使用预测的Kt：p值作为描述符可能有利于使用决策树准确预测Vss。在这项工作中提出的基于决策树的模型具有合理的准确性，并且与文献中报告的Vss种间外推的准确性相似。药物发现中新化合物的Vss估计将受益于能够整合大量不同数据源的方法和灵活的非线性数据挖掘方法，如决策树，它可以产生可解释的模型。决策树用于药物组织分配系数和分布容积的预测。本文的在线版本（doi：10.1186/s13321-015-0054-x）包含补充材料，可供授权用户使用。
Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug’s distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds’ molecular descriptors and the compounds’ tissue:plasma partition coefficients (Kt:p) – often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds’ molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Decision trees for the prediction of tissue partition coefficient and volume of distribution of drugs. The online version of this article (doi:10.1186/s13321-015-0054-x) contains supplementary material, which is available to authorized users.
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影响因子：
5.8
作者：
Ghafourian, Taravat;Barzegar-Jalali, Mohammad;Nokhodchi, Ali
通讯作者：
Nokhodchi, Ali
影响因子：
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影响因子：
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