Computational prediction of blood-brain barrier permeability using decision tree induction.
Computational prediction of blood-brain barrier permeability using decision tree induction.
复制标题
使用决策树诱导对血脑屏障渗透性的计算预测。
DOI：
10.3390/molecules170910429
复制
发表时间：
2012-08-31
期刊：
影响因子：
--
通讯作者：
Huwyler J
中科院分区：
文献类型：
--
作者：
Suenderhauf C;Hammann F;Huwyler J
预测血脑屏障（BBB）的渗透性对于药物开发至关重要，因为如果不首先穿过该屏障，分子就不能在脑实质内表现出药理学活性。然而，由于有限的被动扩散和主动运输的结合，理解渗透过程是复杂的。我们的目的是建立包括主动和被动转运的血脑屏障药物渗透的预测模型。使用大鼠体内表面渗透性乘积（logPS）值作为BBB渗透性的定量参数，编制了153种化合物的数据库。使用开源化学开发工具包（CDK）计算物理化学性质和描述符。通过机器学习范例（决策树归纳）在两个描述符集上实现预测计算模型。建立了校正分类率（CCR）为90%的模型。通过基于蚁群优化（ACO）的二元分类器分析提供了对BBB运输的机制洞察，以识别最具预测性的化学亚结构。决策树揭示了亲脂性（aLogP）和电荷（极性表面积）的描述符，这也是以前描述的被动扩散模型。然而，分子的几何形状和连通性的措施被发现是相关的一个积极的药物转运组件。
Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component.
登录
查看更多内容
影响因子：
5.6
作者：
Hammann, Felix;Suenderhauf, Claudia;Huwyler, Joerg
通讯作者：
Huwyler, Joerg
影响因子：
3.7
作者：
Kelder, J;Grootenhuis, PDJ;Ploemen, JP
通讯作者：
Ploemen, JP
影响因子：
3.8
作者：
Clark, DE
通讯作者：
Clark, DE
影响因子：
4.7
作者：
Dauchy, Sandrine;Dutheil, Fabien;Decleves, Xavier
通讯作者：
Decleves, Xavier
影响因子：
2
作者：
Benedetti, MS;Frigerio, E;Dostert, P
通讯作者：
Dostert, P