一种使用分类树预测类药物化合物血脑屏障通透性的简单方法。
A Simple Method to Predict Blood-Brain Barrier Permeability of Drug- Like Compounds Using Classification Trees.
作者信息
Castillo-Garit Juan A, Casanola-Martin Gerardo M, Le-Thi-Thu Huong, Pham-The Hai, Barigye Stephen J
机构信息
Unidad de Toxicologia Experimental, Universidad de Ciencias Medicas de Villa Clara, Carretera a acueducto y circunvalacion, Santa Clara, Villa Clara. Cuba.
Unidad de Investigacion de Diseno de Farmacos y Conectividad Molecular, Departamento de /Quimica Fisica, Facultad de Farmacia, Universitat de Valencia. Spain.
出版信息
Med Chem. 2017;13(7):664-669. doi: 10.2174/1573406413666170209124302.
BACKGROUND
To know the ability of a compound to penetrate the blood-brain barrier (BBB) is a challenging task; despite the numerous efforts realized to predict/measure BBB passage, they still have several drawbacks.
METHOD
The prediction of the permeability through the BBB is carried out using classification trees. A large data set of 497 compounds (recently published) is selected to develop the tree model.
RESULTS
The best model shows an accuracy higher than 87.6% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 86.1% and 87.9%, correspondingly. We give a brief explanation, in structural terms, of how our model describes the passage of molecules through the BBB. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results.
CONCLUSION
Finally, we can say that, the present model would be a valuable tool in the early stages of drug discovery process of neuropharmaceuticals.
背景
了解一种化合物穿透血脑屏障（BBB）的能力是一项具有挑战性的任务；尽管为预测/测量BBB通透性付出了诸多努力，但这些方法仍存在一些缺陷。
方法
使用分类树对通过BBB的通透性进行预测。选择了一个包含497种化合物的大型数据集（最近发表）来构建树模型。
结果
最佳模型在训练集上的准确率高于87.6%；该模型还通过10倍交叉验证程序进行了验证，并通过一个测试集进行验证，相应地获得了86.1%和87.9%的准确率值。我们从结构角度简要解释了我们的模型如何描述分子通过BBB的过程。此外，将获得的模型与国际文献中先前发表的其他方法进行比较，结果显示更好。
结论
最后，我们可以说，本模型将成为神经药物发现过程早期阶段的一个有价值的工具。
Zotero 插件