Leveraging Graph Attention Networks for Blood-Brain Barrier Permeation Prediction

The development of pharmaceuticals requires an extensive screening of molecules to evaluate their biochemical properties such as absorption, distribution, metabolism, excretion, and toxicity (ADMET). In this work, we explore the combination of Graph Neural Networks (GNNs) for the prediction of ADMET properties, with a specific focus on Blood-Brain Barrier (BBB) Permeation. Our GNN model performs consistently well across all metrics, evaluated on three different benchmark datasets. The main takeaway is the ability to maintain performance on unknown datasets that contain chemically diverse compounds, which we validated using the Tanimoto Similarity comparison. Our results demonstrate that GNNs can reliably predict BBB Permeation. Next steps for improvement include experimenting with different molecular features, model architectures, and exploring transfer learning strategies. Finally, the GNN model we developed for predicting BBB Permeation is designed to be general and can be extended for different small molecule prediction tasks, supporting both SMILES and InChi data formats. Our focus in future experiments is to extend this model to predict other ADMET properties.