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  • https://doi.org/10.1021/acsomega.5c04861Copy DOI Icon

Prediction of Permeabilityand Efflux Using MultitaskLearning

  • Nov 5, 2025
  • ACS Omega
  • Philip Ivers Ohlsson +4 more
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Abstract

In silico prediction of cell membranepermeabilityis crucial in drug discovery, since a compound’s permeationthrough membranes influences parameters such as its in vivo efficacy,bioavailability, and pharmacokinetics. This study investigates theuse of multitask graph neural networks to predict a selection of permeability-relatedendpoints. The study utilized a harmonized, single-laboratory internaldata set of over 10K compounds measured in human colorectal adenocarcinoma(Caco-2) and Madin–Darby canine kidney (MDCK) cell lines, routinelyemployed in experimental assays for drug permeability and efflux.This data set is an order of magnitude larger than comparable publiccollections, thus providing greater statistical power and a consistenterror profile for model development. A series of multitask learning(MTL) models trained on such data were benchmarked against single-taskapproaches and evaluated on an external public data set to investigatethe model’s applicability domain. The comparison between theperformance of single- and multitask models suggests that MTL canachieve higher accuracy by leveraging shared information across endpoints.MTL is also shown to perform better when augmented with molecularfeatures. In particular, the inclusion of pKa and LogD, is shown to improve the accuracy of both permeabilityand efflux endpoints. This work presents benchmarking results of modelsutilizing different data splitting strategies, accompanied by guidelinesfor optimal validation in the context of MTL.

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