In this study, we developed various machine and deep learning models to predict the blood-brain barrier (BBB) permeability of drug molecules and validated model efficacy using a Parallel Artificial Membrane Permeability Assay (PAMPA) on an external set of glioma drugs.

A publicly available database of nearly eight thousand compounds with known BBB permeability was used for model development. Model architectures included in this study are support vector machines (SVMs), deep neural networks (DNNs), graph convolutional neural networks (GCNNs), and transfer learning with DNNs. 30 compounds from the Emory Enriched Bioactive Library were prioritized for in vitro experimental validation with PAMPA, consisting of an artificial lipid membrane that simulates BBB properties.

GCCN’s offer the best overall predictive capability and dipole moment is the most promising quantum chemical property used for transfer learning. The PAMPA assay demonstrates a reliable and efficient way of performing experimental validation for BBB permeability. This study motivates the synergy of computational and experimental methods in screening compounds for CNS-activity.