Wolff, ChristopherNeuenschwander, MartinBeese, Carsten JörnSitani, DivyaRamos, Maria C.Srovnalova, AlzbetaVarela Liste, María JoséPolishchuk, PavelSkopelitou, Katholiki E.Škuta, CtiborStechmann, BahneBrea Floriani, José ManuelClausen, Mads HartvigDzubak, PetrFernández-Godino, RosarioGenilloud, OlgaHajduch, MarianLoza García, María IsabelLehmann, MartinKries, Jens Peter vonSun, HanSchmied, Christopher2026-01-162026-01-162025-05-16Christopher Wolff, Martin Neuenschwander, Carsten Jörn Beese, Divya Sitani, Maria C. Ramos, Alzbeta Srovnalova, María José Varela, Pavel Polishchuk, Katholiki E. Skopelitou, Ctibor Škuta, Bahne Stechmann, José Brea, Mads Hartvig Clausen, Petr Dzubak, Rosario Fernández-Godino, Olga Genilloud, Marian Hajduch, María Isabel Loza, Martin Lehmann, Jens Peter von Kries, Han Sun, Christopher Schmied, Morphological profiling data resource enables prediction of chemical compound properties, iScience, Volume 28, Issue 5, 2025, 112445, ISSN 2589-0042, https://doi.org/10.1016/j.isci.2025.112445https://hdl.handle.net/10347/45235Morphological profiling with the Cell Painting assay has emerged as a promising method in drug discovery research. The assay captures morphological changes across various cellular compartments enabling the rapid prediction of compound bioactivity. We present a comprehensive morphological profiling resource using the carefully curated and well-annotated EU-OPENSCREEN Bioactive compounds. The data were generated across four imaging sites with high-throughput confocal microscopes using the Hep G2 as well as the U2 OS cell lines. We employed an extensive assay optimization process to achieve high data quality across the different sites. An analysis of the extracted profiles validates the robustness of the generated data. We used this resource to compare the morphological features of the different cell lines. By correlating the profiles with overall activity, cellular toxicity, several specific mechanisms of action (MOAs), and protein targets, we demonstrate the dataset’s potential for facilitating more extensive exploration of MOAseng© 2025 The Author(s). Published by Elsevier Inc. This is an open access article distributed under the terms of the Creative Commons CC-BY licenseAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Chemical compound propertiesSmall molecule librariesCell PaintingMorphological profiling data resource enables prediction of chemical compound propertiesjournal article10.1016/j.isci.2025.1124452589-0042open access