Computational prediction of blood-brain barrier permeability using decision tree induction.

ABSTRACT: Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learni

SUBMITTER: Suenderhauf C 

PROVIDER: S-EPMC6269008 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature