biostudies-literatureDing YNCATS NIH HHSNIA NIH HHSNIH HHS2826-2831https://www.ebi.ac.uk/biostudies/studies/S-EPMC9113341biostudies-literatureUnknown38(10)<h4>Motivation</h4>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.<h4>ResultBioinformatics (Oxford, England)Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules.PMC9113341U01 TR002062R01AG066749R01 AG066749Ding YJiang XKim YfalseRelational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules.<h4>Motivation</h4>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.<h4>Result2022-01-01T00:00:00Z2022 May2025-04-05T09:55:44.351Z2025-04-05T09:55:44.351ZS-EPMC91133413556119910.1093/bioinformatics/btac211