2026-04-13T02:58:54Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/2079222025-11-20T15:05:11Zcom_2072_1057col_2072_478916col_2072_478917

00925njm 22002777a 4500 dc Hu, Huabin author Tjaden, Amelie author Knapp, Stefan author Antolin, Albert A. author Müller, Susanne author 2024-02-22T10:18:03Z 2024-10-04T05:10:08Z 2023-12-21 2024-02-19T09:44:57Z Drug-induced phospholipidosis (DIPL), characterized by excessive accumulation of phospholipids in lysosomes, can lead to clinical adverse effects. It may also alter phenotypic responses in functional studies using chemical probes. Therefore, robust methods are needed to predict and quantify phospholipidosis (PL) early in drug discovery and in chemical probe characterization. Here, we present a versatile high-content live-cell imaging approach, which was used to evaluate a chemogenomic and a lysosomal modulation library. We trained and evaluated several machine learning models using the most comprehensive set of publicly available compounds and interpreted the best model using SHapley Additive exPlanations (SHAP). Analysis of high-quality chemical probes extracted from the Chemical Probes Portal using our algorithm revealed that closely related molecules, such as chemical probes and their matched negative controls can differ in their ability to induce PL, highlighting the importance of identifying PL for robust target validation in chemical biology. Lipoïdosi Aprenentatge automàtic Lipidoses Machine learning A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis