Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules.

ABSTRACT:

Motivation

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.

Result

SUBMITTER: Ding Y 

PROVIDER: S-EPMC9113341 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature