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 learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component.

A Computational Study of Anticancer Activity of Curcumin Derivatives Using in silico Drug Designing and Molecular Docking Tools

“…Number of H-bond acceptors must be less than or equal to 10; is satisfied by all ligands. Number of H-bond donors must be less than or equal to 5; satisfied by all ligands [17]. The molecular properties of the standard drug, megestrol acetate was also calculated using the same tool and compared with the values of ligands calculated.…”

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