Blood-Brain Barrier Passage Prediction Using Decision Tree

In this report, the blood brain barrier (BBB) permeability prediction is carried out using a decision tree. A recently published data set of 497 compounds is selected to develop the tree model. The developed model shows an accuracy of 87.66% for training set; 86.09% in the 10-fold cross-validation procedure and 87.93% for the test set. Some structural explanation of how our model describe the passage of molecules through the BBB is given. Moreover, a comparison with other approaches is carried out showing good behaviour of our method. Finally, we can say that, the present results could represent a useful tools available and reproducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects.