ArticleNature communications2023
CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Semantic knowledge improves molecular machine learning for chemical toxicity prediction.iScience · 2026Article
- Empowering chemical structures with biological insights for scalable phenotypic virtual screening.Bioinformatics (Oxford, England) · 2026Article
- Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action.Molecular systems biology · 2026Article
- Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling.PLoS computational biology · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- PhenoModel: A multimodal phenotypic drug design foundation model for discovering novel potential inhibitors of multiple cancer cells.Acta pharmaceutica Sinica. B · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Counting cells can accurately predict small-molecule bioactivity benchmarks.Nature communications · 2026Article
- Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.Communications biology · 2026Article
- PhenoProfiler: advancing phenotypic learning for image-based drug discovery.Nature communications · 2025Article
- Confounder-aware foundation modeling for accurate phenotype profiling in cell imaging.Npj imaging · 2025Article
- Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling.Scientific reports · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Morphological profiling data resource enables prediction of chemical compound properties.iScience · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Parameter efficient multi-model vision assistant for polymer solvation behaviour inference.npj computational materials · 2025Article
- Integrating Biological Knowledge for Robust Microscopy Image Profiling onProceedings. IEEE International Conference on Computer Vision · 2025Article
- MorphoDiff: Cellular Morphology Painting with Diffusion Models.bioRxiv : the preprint server for biology · 2024Article
- Decoding phenotypic screening: A comparative analysis of image representations.Computational and structural biotechnology journal · 2024Article
- Learning Molecular Representation in a Cell.ArXiv · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of bioimage analysis is currently impacted by a profound transformation, driven by the advancements in imaging technologies and artificial intelligence. The emergence of multi-modal AI systems could allow extracting and utilizing knowledge from bioimaging databases based on information from other data modalities. We leverage the multi-modal contrastive learning paradigm, which enables the embedding of both bioimages and chemical structures into a unified space by means of bioimage and molecular structure encoders. This common embedding space unlocks the possibility of querying bioimaging databases with chemical structures that induce different phenotypic effects. Concretely, in this work we show that a retrieval system based on multi-modal contrastive learning is capable of identifying the correct bioimage corresponding to a given chemical structure from a database of ~2000 candidate images with a top-1 accuracy >70 times higher than a random baseline. Additionally, the bioimage encoder demonstrates remarkable transferability to various further prediction tasks within the domain of drug discovery, such as activity prediction, molecule classification, and mechanism of action identification. Thus, our approach not only addresses the current limitations of bioimaging databases but also paves the way towards foundation models for microscopy images.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.