A robust and interpretable graph neural network-based protocol for predicting p-glycoprotein substrates.

ABSTRACT: P-glycoprotein (P-gp), a key member of the ATP-binding cassette (ABC) transporter family, plays a significant role in drug absorption and distribution by binding to diverse xenobiotics and actively transporting them out of cells. Given P-gp's widespread expression, including its critical presence at the blood-brain barrier, identifying whether a compound functions as a P-gp substrate or inhibitor is essential in drug development to evaluate its ability to penetrate the central nervous system. However, most studies on P-gp focus on inhibitor models rather than substrate models. This study presents a robust graph neural network approach to predict P-gp substrates, leveraging graph convolutional networks, AttentiveFP, and an ensemble model. Using a dataset of 1995 drug molecules (1202 substra

SUBMITTER: Hsu KC 

PROVIDER: S-EPMC12318477 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature