In-silico prediction of blood–brain barrier permeability

Abstract

The ability of penetration of the blood–brain barrier is one of the significant properties of a drug or drug-like compound for the central nervous system (CNS), which is commonly expressed by log BB (log BB = log (C _brain/C _blood)). In this work, a dataset of 320 compounds with log BB values was split into a training set including 198 compounds and a test set including 122 compounds according to their structure properties by a Kohonen's self-organizing map (SOM). Each molecule was represented by global and shape descriptors, 2D autocorrelation descriptors and RDF descriptors calculated by ADRIANA.Code. Several quantitative models for prediction of log BB were built by a multilinear regression (MLR), a support vector machine (SVM) and an artificial neural network (ANN) analysis. The models show good prediction performance on the test set compounds.

Keywords:

• BBB (blood–brain barrier)
• quantitative structure-activity relationships (QSAR)
• Kohonen's self-organizing map (SOM)
• multilinear regression (MLR)
• support vector machine (SVM)
• artificial neural network (ANN)

Acknowledgments

This work was supported by the National Natural Science Foundation of China (20605003, 20975011 and 81273631). The authors thank Prof. J. Gasteiger and the Molecular Networks GmbH, Erlangen, Germany for providing the programs ADRIANA.Code and SONNIA for our scientific work.