Geometric Multi-color Message Passing Graph Neural Networks for Blood-brain Barrier Permeability Prediction

TL;DR

This paper introduces GMC-MPNN, a geometric multi-color message-passing graph neural network that incorporates 3D atomic geometry to improve blood-brain barrier permeability prediction, outperforming existing models.

Contribution

The paper presents a novel GNN framework that explicitly models atomic geometric features and long-range interactions, enhancing BBB permeability prediction accuracy.

Findings

GMC-MPNN achieves higher AUC-ROC scores (0.947 and 0.9212) than previous models.

The model accurately regresses permeability with RMSE of 0.5628 and Pearson correlation of 0.6947.

Ablation studies show the importance of geometric and atom-pair features for prediction.

Abstract

Accurate prediction of blood-brain barrier permeability (BBBP) is essential for central nervous system (CNS) drug development. While graph neural networks (GNNs) have advanced molecular property prediction, they often rely on molecular topology and neglect the three-dimensional geometric information crucial for modeling transport mechanisms. This paper introduces the geometric multi-color message-passing graph neural network (GMC-MPNN), a novel framework that enhances standard message-passing architectures by explicitly incorporating atomic-level geometric features and long-range interactions. Our model constructs weighted colored subgraphs based on atom types to capture the spatial relationships and chemical context that govern BBB permeability. We evaluated GMC-MPNN on three benchmark datasets for both classification and regression tasks, using rigorous scaffold-based splitting to ensure a robust assessment of generalization. The results demonstrate that GMC-MPNN consistently outperforms existing state-of-the-art models, achieving superior performance in both classifying compounds as permeable/non-permeable (AUC-ROC of 0.947 and 0.9212) and in regressing continuous permeability values (RMSE of 0.5628, Pearson correlation of 0.6947). An ablation study further quantified the impact of specific atom-pair interactions, revealing that the model's predictive power derives from its ability to learn from both common and rare, but chemically significant, functional motifs. By integrating spatial geometry into the graph representation, GMC-MPNN sets a new performance benchmark and offers a more accurate and generalizable tool for drug discovery pipelines.