{"entity": "researcher", "timestamp": "2026-08-23T09:25:43.718Z", "family": "M\u00fcllers", "given": "Erik", "initials": "E", "orcid": "0000-0002-2176-3248", "affiliations": ["Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden. mullers.erik@gmail.com."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/e70b7d7a91f44d1b8e9d62987abeecf7.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/e70b7d7a91f44d1b8e9d62987abeecf7"}}, "publications": [{"entity": "publication", "iuid": "674a8da7a20e4382b5875e0d665c552a", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/674a8da7a20e4382b5875e0d665c552a.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/674a8da7a20e4382b5875e0d665c552a"}}, "title": "Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity.", "authors": [{"family": "Fredin Haslum", "given": "Johan", "initials": "J"}, {"family": "Lardeau", "given": "Charles-Hugues", "initials": "CH"}, {"family": "Karlsson", "given": "Johan", "initials": "J"}, {"family": "Turkki", "given": "Riku", "initials": "R"}, {"family": "Leuchowius", "given": "Karl-Johan", "initials": "KJ", "orcid": "0009-0008-2794-1931", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/0dec547e82a14fa9b87474c0aef5db3e.json"}}, {"family": "Smith", "given": "Kevin", "initials": "K"}, {"family": "M\u00fcllers", "given": "Erik", "initials": "E", "orcid": "0000-0002-2176-3248", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e70b7d7a91f44d1b8e9d62987abeecf7.json"}}], "type": "journal article", "published": "2024-04-24", "journal": {"title": "Nat Commun", "issn": "2041-1723", "volume": "15", "issue": "1", "pages": "3470", "issn-l": "2041-1723"}, "abstract": "Identifying active compounds for a target is a time- and resource-intensive task in early drug discovery. Accurate bioactivity prediction using morphological profiles could streamline the process, enabling smaller, more focused compound screens. We investigate the potential of deep learning on unrefined single-concentration activity readouts and Cell Painting data, to predict compound activity across 140 diverse assays. We observe an average ROC-AUC of 0.744 \u00b1 0.108 with 62% of assays achieving \u22650.7, 30% \u22650.8, and 7% \u22650.9. In many cases, the high prediction performance can be achieved using only brightfield images instead of multichannel fluorescence images. A comprehensive analysis shows that Cell Painting-based bioactivity prediction is robust across assay types, technologies, and target classes, with cell-based assays and kinase targets being particularly well-suited for prediction. Experimental validation confirms the enrichment of active compounds. Our findings indicate that models trained on Cell Painting data, combined with a small set of single-concentration data points, can reliably predict the activity of a compound library across diverse targets and assays while maintaining high hit rates and scaffold diversity. This approach has the potential to reduce the size of screening campaigns, saving time and resources, and enabling primary screening with more complex assays.", "doi": "10.1038/s41467-024-47171-1", "pmid": "38658534", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11043326"}, {"db": "pii", "key": "10.1038/s41467-024-47171-1"}], "notes": [], "created": "2026-08-20T08:53:12.539Z", "modified": "2026-08-20T08:53:12.830Z"}, {"entity": "publication", "iuid": "051c04f6a0054303b8d7fe4c57714b5e", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/051c04f6a0054303b8d7fe4c57714b5e.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/051c04f6a0054303b8d7fe4c57714b5e"}}, "title": "FRET-Based Sorting of Live Cells Reveals Shifted Balance between PLK1 and CDK1 Activities During Checkpoint Recovery.", "authors": [{"family": "Lafranchi", "given": "Lorenzo", "initials": "L", "orcid": "0000-0001-8234-4162", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fa4a7b64902645119ca2a2a41e316f94.json"}}, {"family": "M\u00fcllers", "given": "Erik", "initials": "E", "orcid": "0000-0002-2176-3248", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e70b7d7a91f44d1b8e9d62987abeecf7.json"}}, {"family": "Rutishauser", "given": "Dorothea", "initials": "D", "orcid": "0000-0003-2303-103X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3897b4ad508146d1949845d65cf149b1.json"}}, {"family": "Lindqvist", "given": "Arne", "initials": "A"}], "type": "journal article", "published": "2020-09-19", "journal": {"title": "Cells", "issn": "2073-4409", "volume": "9", "issue": "9", "issn-l": "2073-4409"}, "abstract": "Cells recovering from the G2/M DNA damage checkpoint rely more on Aurora A-PLK1 signaling than cells progressing through an unperturbed G2 phase, but the reason for this discrepancy is not known. Here, we devised a method based on a FRET reporter for PLK1 activity to sort cells in distinct populations within G2 phase. We employed mass spectroscopy to characterize changes in protein levels through an unperturbed G2 phase and validated that ATAD2 levels decrease in a proteasome-dependent manner. Comparing unperturbed cells with cells recovering from DNA damage, we note that at similar PLK1 activities, recovering cells contain higher levels of Cyclin B1 and increased phosphorylation of CDK1 targets. The increased Cyclin B1 levels are due to continuous Cyclin B1 production during a DNA damage response and are sustained until mitosis. Whereas partial inhibition of PLK1 suppresses mitotic entry more efficiently when cells recover from a checkpoint, partial inhibition of CDK1 suppresses mitotic entry more efficiently in unperturbed cells. Our findings provide a resource for proteome changes during G2 phase, show that the mitotic entry network is rewired during a DNA damage response, and suggest that the bottleneck for mitotic entry shifts from CDK1 to PLK1 after DNA damage.", "doi": "10.3390/cells9092126", "pmid": "32961751", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC7564076"}, {"db": "pii", "key": "cells9092126"}], "notes": [], "created": "2026-08-21T13:02:27.035Z", "modified": "2026-08-21T13:02:27.119Z"}]}