  Evaluating the blood-brain barrier (BBB) permeability of drug molecules is a
critical step in brain drug development. Traditional methods for the evaluation
require complicated in vitro or in vivo testing. Alternatively, in silico
predictions based on machine learning have proved to be a cost-efficient way to
complement the in vitro and in vivo methods. However, the performance of the
established models has been limited by their incapability of dealing with the
interactions between drugs and proteins, which play an important role in the
mechanism behind the BBB penetrating behaviors. To address this limitation, we
employed the relational graph convolutional network (RGCN) to handle the
drug-protein interactions as well as the properties of each individual drug.
The RGCN model achieved an overall accuracy of 0.872, an AUROC of 0.919 and an
AUPRC of 0.838 for the testing dataset with the drug-protein interactions and
the Mordred descriptors as the input. Introducing drug-drug similarity to
connect structurally similar drugs in the data graph further improved the
testing results, giving an overall accuracy of 0.876, an AUROC of 0.926 and an
AUPRC of 0.865. In particular, the RGCN model was found to greatly outperform
the LightGBM base model when evaluated with the drugs whose BBB penetration was
dependent on drug-protein interactions. Our model is expected to provide
high-confidence predictions of BBB permeability for drug prioritization in the
experimental screening of BBB-penetrating drugs.
