ReviewBriefings in bioinformatics2024
Morphological profiling for drug discovery in the era of deep learning.
Review in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed.
- Aporphine AlkaloidPharmaceuticals (Basel, Switzerland) · 2026Article
- Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling.PLoS computational biology · 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
- Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Single-cell hit calling in high-content imaging screens with Buscar.bioRxiv : the preprint server for biology · 2026Article
- Precision oncology in the age of AI: lessons from AI-driven drug discovery and clinical translation.BJC reports · 2026Review
- Integrating Human Intestinal Organoids into FDA's New Approach Methodologies for Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Smart Nanoparticles Are Not Smart Enough (Yet): A Cell-Aware View of Cancer Nanomedicine.Cells · 2026Article
- PhenoModel: A multimodal phenotypic drug design foundation model for discovering novel potential inhibitors of multiple cancer cells.Acta pharmaceutica Sinica. B · 2026Article
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Seeing structure, losing sight: The case for morphological thinking in the age of integration.Anatomical record (Hoboken, N.J. : 2007) · 2026Article
- SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology.bioRxiv : the preprint server for biology · 2025Article
- Image-Based Profiling in Live Cells Using Live Cell Painting.Bio-protocol · 2025Article
- Designing high-affinity 3D drug molecules via geometric spatial perception diffusion model.Briefings in bioinformatics · 2025Article
- From bench to bedside: nanomedicine development for intracerebral hemorrhage - exploring microenvironment, innovation, and translation.Journal of nanobiotechnology · 2025Review
- Article
- Linking autism risk genes to morphological and pharmaceutical screening by high-content imaging: Future directions and opinion.Psychiatry and clinical neurosciences · 2025Review
- Precision Neuro-Oncology in Glioblastoma: AI-Guided CRISPR Editing and Real-Time Multi-Omics for Genomic Brain Surgery.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.
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Registered trials
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.