Self-Supervised Molecular Models for P-Glycoprotein Substrate Prediction Using Transporter Assay Data

P-glycoprotein efflux can strongly constrain oral absorption, brain penetration, and intracellular drug exposure. Computational substrate prediction is therefore an important early filter for molecules likely to face transporter-mediated disposition liabilities. Most transporter models rely on limited labeled assay data and are often trained directly on endpoint-specific measurements. This ignores the broader chemical information contained in large collections of unlabeled molecular structures.