Prediction of Permeability and Efflux Using Multitask Learning.

ABSTRACT: In silico prediction of cell membrane permeability is crucial in drug discovery, since a compound's permeation through membranes influences parameters such as its in vivo efficacy, bioavailability, and pharmacokinetics. This study investigates the use of multitask graph neural networks to predict a selection of permeability-related endpoints. The study utilized a harmonized, single-laboratory internal data set of over 10K compounds measured in human colorectal adenocarcinoma (Caco-2) and Madin-Darby canine kidney (MDCK) cell lines, routinely employed in experimental assays for drug permeability and efflux. This data set is an order of magnitude larger than comparable public collections, thus providing greater statistical power and a consistent error profile for model development. A

SUBMITTER: Ohlsson PI 

PROVIDER: S-EPMC12631307 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature