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A divergent intermediate strategy yields biologically diverse pseudo-natural products

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A Synergistic Anticancer FAK and HDAC Inhibitor Combination Discovered by a Novel Chemical–Genetic High-Content Phenotypic Screen

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Breaking Bad Proteins—Discovery Approaches and the Road to Clinic for Degraders

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Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: Retinoic acid as an example

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Multiparametric High-Content Cell Painting Identifies Copper Ionophores as Selective Modulators of Esophageal Cancer Phenotypes

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Advancements in Assay Technologies and Strategies to Enable Drug Discovery

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Synthetic gene circuits as tools for drug discovery

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Pseudo Natural Products—Chemical Evolution of Natural Product Structure

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Neurodevelopmental copy-number variants: A roadmap to improving outcomes by uniting patient advocates, researchers, and clinicians for collective impact

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Inhibition of Aryl Hydrocarbon Receptor (AhR) Expression Disrupts Cell Proliferation and Alters Energy Metabolism and Fatty Acid Synthesis in Colon Cancer Cells

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Combining molecular and cell painting image data for mechanism of action prediction

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Morphological Profiling Identifies the Motor Protein Eg5 as Cellular Target of Spirooxindoles

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Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

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High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells

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Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts

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Pseudonatural Products for Chemical Biology and Drug Discovery

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Predicting Chemical-Induced Liver Toxicity Using High-Content Imaging Phenotypes and Chemical Descriptors: A Random Forest Approach

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Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging

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High-content microscopy reveals a morphological signature of bortezomib resistance

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RxRx3: Phenomics Map of BiologyPreprint

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Assessing the performance of the Cell Painting assay across different imaging systems

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Motion sensing superpixels (MOSES) is a systematic computational framework to quantify and discover cellular motion phenotypes

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Multiscale modelling of drug mechanism and safety

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What advances may the future bring to the diagnosis, treatment, and care of male sexual and reproductive health?

Barratt, C. L. R., Wang, C., Baldi, E., Toskin, I., Kiarie, J. N., & Lamb, D. J. (2022) · Fertility and Sterility · doi:10.1016/j.fertnstert.2021.12.013 · Cited by 22

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

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Deep representation learning determines drug mechanism of action from cell painting images

Wong, D. R., Logan, D. J., Hariharan, S., Stanton, R. V., Clevert, D.-A., & Kiruluta, A. J. M. (2023) · Digital Discovery · doi:10.1039/d3dd00060e · Cited by 22

Compound Functional Prediction Using Multiple Unrelated Morphological Profiling Assays

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Design, Synthesis, and Phenotypic Profiling of Pyrano‐Furo‐Pyridone Pseudo Natural Products

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In vitro disease models 4.0 via automation and high-throughput processing

Eggert, S., & Hutmacher, D. W. (2019) · Biofabrication · doi:10.1088/1758-5090/ab296f · Cited by 24

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Pasquer, Q. T. L., Tsakoumagkos, I. A., & Hoogendoorn, S. (2020) · Molecules · doi:10.3390/molecules25235702 · Cited by 21

Interpreting Image‐based Profiles using Similarity Clustering and Single‐Cell Visualization

Garcia-Fóssa, F., Cruz, M. C., Haghighi, M., de Jesus, M. B., Singh, S., Carpenter, A. E., & Cimini, B. A. (2023) · Current Protocols · doi:10.1002/cpz1.713 · Cited by 21

Inference of long-range cell-cell force transmission from ECM remodeling fluctuations

Nahum, A., Koren, Y., Ergaz, B., Natan, S., Miller, G., Tamir, Y., Goren, S., Kolel, A., Jagadeeshan, S., Elkabets, M., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05179-1 · Cited by 20

Scalable, compressed phenotypic screening using pooled perturbations

Liu, N., Kattan, W. E., Mead, B. E., Kummerlowe, C., Cheng, T., Ingabire, S., Cheah, J. H., Soule, C. K., Vrcic, A., McIninch, J. K., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02403-z · Cited by 25

Systematic Diversity-Oriented Synthesis of Reduced Flavones from γ-Pyrones to Probe Biological Performance Diversity

Gerlach, E. M., Korkmaz, M. A., Pavlinov, I., Gao, Q., & Aldrich, L. N. (2019) · ACS Chemical Biology · doi:10.1021/acschembio.9b00294 · Cited by 21

A novel therapeutic antibody screening method using bacterial high-content imaging reveals functional antibody binding phenotypes of Escherichia coli ST131

Maes, M., Dyson, Z. A., Smith, S. E., Goulding, D., Ludden, C., Baker, S. G., Kellam, P., Reece, S. T., Gordon Dougan, B., & Scott, J. B. (2020) · Scientific Reports · doi:10.1038/s41598-020-69300-8 · Cited by 21

Innovative Approaches for Drug Discovery: Quantifying Drug Distribution and Response with Raman Imaging

Dunnington, E. L., Wong, B. S., & Fu, D. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c01413 · Cited by 25

Label-free live cell recognition and tracking for biological discoveries and translational applications

Chen, B., Yin, Z., Ng, W., Wang, D. M., Tuan, R. S., Bise, R., & Ker, D. F. E. (2024) · npj Imaging · doi:10.1038/s44303-024-00046-y · Cited by 23

Machine learning in microscopy – insights, opportunities and challenges

Cunha, I., Latron, E., Bauer, S., Sage, D., & Griffié, J. (2024) · Journal of Cell Science · doi:10.1242/jcs.262095 · Cited by 22

Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis

Yang, K., Goldman, S., Jin, W., Lu, A. X., Barzilay, R., Jaakkola, T., & Uhler, C. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00662 · Cited by 26

Leveraging Cell Painting Images to Expand the Applicability Domain and Actively Improve Deep Learning Quantitative Structure–Activity Relationship Models

Herman, D., Kańduła, M. M., Freitas, L. G. A., van Dongen, C., Van, T. L., Mesens, N., Jaensch, S., Gustin, E. M., Micholt, L., Lardeau, C.-H., et al. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00404 · Cited by 21

Orpinolide disrupts a leukemic dependency on cholesterol transport by inhibiting OSBP

Cigler, M., Imrichová, H., Frommelt, F., Caramelle, L., Depta, L., Rukavina, A., Kagiou, C., Hannich, J. T., Mayor‐Ruiz, C., Superti‐Furga, G., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01614-4 · Cited by 20

OME-Zarr: a cloud-optimized bioimaging file format with international community supportPreprint

Moore, J., Basurto-Lozada, D., Besson, S., Bogovic, J., Bragantini, J., Brown, E. M., Burel, J., Moreno, X. C., de Medeiros, G., Diel, E. E., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.17.528834 · Cited by 20

A Decade in a Systematic Review: The Evolution and Impact of Cell PaintingPreprint

Seal, S., Trapotsi, M., Spjuth, O., Singh, S., Puigvert, J. C., Greene, N., Bender, A., & Carpenter, A. E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.04.592531 · Cited by 22

A statistical framework for high-content phenotypic profiling using cellular feature distributions

Pearson, Y. E., Kremb, S., Butterfoss, G. L., Xie, X., Fahs, H., & Gunsalus, K. C. (2022) · Communications Biology · doi:10.1038/s42003-022-04343-3 · Cited by 19

Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics

Ni, S., Kong, X., Zhang, Y., Chen, Z., Wang, Z., Fu, Z., Huo, R., Tong, X., Qu, N., Wu, X., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100655 · Cited by 19

SPACe: an open-source, single-cell analysis of Cell Painting data

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Morphological profiling data resource enables prediction of chemical compound properties

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Pluripotent Stem Cell-Derived Human Tissue: Platforms to Evaluate Drug Metabolism and Safety

Meseguer-Ripolles, J., Khetani, S. R., Blanco, J. G., Iredale, M., & Hay, D. C. (2017) · The AAPS Journal · doi:10.1208/s12248-017-0171-8 · Cited by 19

Optimizing the Cell Painting assay for image-based profiling

Cimini, B. A., Chandrasekaran, S. N., Kost-Alimova, M., Miller, L., Goodale, A., Fritchman, B., Byrne, P., Garg, S., Jamali, N., Logan, D. J., et al. (2023) · Nature Protocols · doi:10.1038/s41596-023-00840-9 · Cited by 177

Cell Painting: a decade of discovery and innovation in cellular imaging

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Cell Painting PLUS: An Iterative Staining‐Elution Protocol for High‐Content Phenotypic Screenings

Wedler, M., von Coburg, E., Muiño, J. M., Valentin, L., Körber, N., Dunst, S., & Liu, S. (2026) · Current Protocols · doi:10.1002/cpz1.70368

Morphological Profiling in 2D High Content Screening Assays – Case Studies and Applications

Graham, R. E. (2025) · Royal Society of Chemistry eBooks · doi:10.1039/9781837676941-00125

Multiplex Cytological Profiling Assay to Measure Diverse Cellular States

Gustafsdottir, S. M., Ljosa, V., Sokolnicki, K. L., Anthony Wilson, J., Walpita, D., Kemp, M. M., Petri Seiler, K., Carrel, H. A., Golub, T. R., Schreiber, S. L., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080999 · Cited by 291