In-silico prediction of blood-brain barrier permeability
The ability of penetration of the bloodbrain 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.
kohonen's self-organizing map (som) multilinear regression (mlr) support vector machine (svm) artificial neural network (ann) bbb (blood-brain barrier) quantitative structure-activity relationships (qsar)