CNS Permeability of Drugs Predicted by a Decision Tree
Claudia Andrés, Michael Christopher Hutter · QSAR & Combinatorial Science · 2006
Abstract To predict the ability of drug‐like molecules to penetrate the Central Nervous System (CNS), a decision tree was generated. This algorithm was designed to make a straight forward yes/no decision about the permeability of the blood‐brain barrier for a given substance, based on the numerical criteria of a large variety of molecular descriptors. The decision tree achieved a prediction accuracy of 96% for the 186 compounds of the training set and 84% for the test set comprising 38 molecules. We found that CNS+drugs are predicted with a higher accuracy (>94%) than CNS‐ substances (>89%).