Center for Pharmaceutical Innovation and Research, University of Kentucky, 789 South Limestone Street, Lexington, Kentucky, 40536, USA.
Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, Kentucky, 40536, USA.
AAPS J. 2018 Mar 21;20(3):54. doi: 10.1208/s12248-018-0215-8.
Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.
血脑屏障（BBB）通透性是决定化合物是否能有效进入大脑的关键性质，对于以大脑为靶点的药物发现来说，这是必须要考虑的一个性质。目前已经有几种计算方法被用于预测 BBB 通透性，其中支持向量机（SVM）作为一种基于核的机器学习方法，在该领域得到了广泛应用。对于 SVM 的训练和预测，化合物的特征是由分子描述符来表示的。一些 SVM 模型是基于使用基于分子性质的描述符（包括 1D、2D 和 3D 描述符）或基于片段的描述符（也称为分子指纹）。描述符的选择对于 SVM 模型的性能至关重要。在这项研究中，我们旨在通过结合基于分子性质描述符和指纹的所有特征，开发一种普遍适用的新 SVM 模型，以提高对 BBB 通透性预测的准确性。结果表明，与目前可用的 BBB 通透性预测模型相比，我们的 SVM 模型具有更高的准确性。
