ArticleNature communications2024
Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Leveraging Morphological Profiling for Mechanistic Elucidation of Metal-Based Anticancer Compounds.JACS Au · 2026Article
- Retrieval-Based Evaluation of Cell Painting Feature Spaces Reveals Differences in the Preservation of Biologically Meaningful Phenotypic Similarity.International journal of molecular sciences · 2026Article
- Accurate prediction of activity cliff compounds based on bioactivity profiles depends on assay nearest neighbor relationships.Journal of cheminformatics · 2026Article
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry.Journal of cheminformatics · 2026Article
- PhenoModel: A multimodal phenotypic drug design foundation model for discovering novel potential inhibitors of multiple cancer cells.Acta pharmaceutica Sinica. B · 2026Article
- A Framework for Autonomous AI-Driven Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- Discovery of TDP-43 aggregation inhibitorsbioRxiv : the preprint server for biology · 2026Article
- Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.Communications biology · 2026Article
- Therapeutic target database 2026: facilitating targeted therapies and precision medicine.Nucleic acids research · 2026Article
- Determination of the Lethal Concentrations of Two Phenolic Acid Derivatives Originated From the Edible Red Marine Macroalga (Food science & nutrition · 2026Article
- Artificial Intelligence-Based In Silico Evaluation of the Pharmacological Potential and In Vitro Anti-Malignancy Effect in the Human Glioblastoma Cell Line of the Hydrate of the Coumarin Compound Meranzin Originating From the Edible Macroalga (Food science & nutrition · 2026Article
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Smaller scale, same impact: replicating high-throughput phenotypic profiling in a medium-throughput lab for use in chemical risk assessment.Archives of toxicology · 2025Article
- Confounder-aware foundation modeling for accurate phenotype profiling in cell imaging.Npj imaging · 2025Article
- Computational prediction of mutagenicity through comprehensive cell painting analysis.Mutagenesis · 2025Article
- Pipeline to evaluate YAP-TEAD inhibitors indicates TEAD inhibition repressesLife science alliance · 2025Article
- Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning.Nature communications · 2025Article
- Evaluation of DNA encoded library and machine learning model combinations for hit discovery.npj drug discovery · 2025Article
- Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.Communications biology · 2025Article
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Authors and funding
7 authors.
Funding
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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 ± 0.108 with 62% of assays achieving ≥0.7, 30% ≥0.8, and 7% ≥0.9. 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.
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