BiGranMolNet: A deep learning method for predicting blood-brain barrier permeability based on Bi-Granularity Molecular Graphs

Objective

The selective permeability of the blood-brain barrier (BBB) hinders the delivery of central nervous system (CNS) drugs to brain targets. It is therefore crucial to determine BBB permeability during CNS drug development.

Methods

We propose BiGranMolNet (Bi-Granularity Molecular Graph Network), a BBB permeability prediction model based on graph convolutional networks. We integrated datasets from multiple sources to construct comprehensive benchmarks containing 2148 regression samples and 16,904 classification samples. Molecular graphs were constructed at two granularities (atom-level and motif-level), and graph convolutional networks were used to learn representations for each granularity. A cross-attention mechanism fused the dual-granularity features, and a weighted loss function was introduced to mitigate class imbalance.

Results

In 5-fold cross-validation and structure-aware evaluations, BiGranMolNet achieved stable and competitive performance on both regression and classification tasks.

Conclusions

By integrating molecular structural information at atomic and motif levels, BiGranMolNet provides a reliable computational tool for BBB permeability prediction. The proposed framework can support early-stage CNS drug screening by prioritizing compounds with favorable brain exposure potential and by offering structure-aware clues for molecular optimization.