ArticleRSC medicinal chemistry2024
Identification of lysosomotropism using explainable machine learning and morphological profiling cell painting data.
Article in RSC medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Targeting a Glutamic Acid in PDEδ with Fluoromethyl-Aryl Electrophiles Impairs K-Ras Signaling.Journal of medicinal chemistry · 2026Article
- Microwave-assisted synthesis of new triangulenium dyes for lifetime imaging microscopy (FLIM).Scientific reports · 2025Article
- Drug-induced phospholipidosis as an artifact in antiviral drug repurposing.bioRxiv : the preprint server for biology · 2025Article
- Lysosomal membrane permeabilization enhances the anticancer effects of POLR1 (RNA polymerase I) transcription inhibitors.Autophagy · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
Lysosomotropism is a phenomenon of diverse pharmaceutical interests because it is a property of compounds with diverse chemical structures and primary targets. While it is primarily reported to be caused by compounds having suitable lipophilicity and basicity values, not all compounds that fulfill such criteria are in fact lysosomotropic. Here, we use morphological profiling by means of the cell painting assay (CPA) as a reliable surrogate to identify lysosomotropism. We noticed that only 35% of the compound subset with matching physicochemical properties show the lysosomotropic phenotype. Based on a matched molecular pair analysis (MMPA), no key substructures driving lysosomotropism could be identified. However, using explainable machine learning (XML), we were able to highlight that higher lipophilicity, basicity, molecular weight, and lower topological polar surface area are among the important properties that induce lysosomotropism in the compounds of this subset.
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Registered trials
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