ReviewCurrent opinion in systems biology2018
Machine learning and image-based profiling in drug discovery.
Review in Current opinion in systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
71 citing papers in PubMed.
- Detection of malaria infection from parasite-free blood smears.Malaria journal · 2026Article
- Transitive prediction of small-molecule function through alignment of high-content screening resources.Nature biotechnology · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology.Briefings in bioinformatics · 2026Review
- Image-Based Profiling in Live Cells Using Live Cell Painting.Bio-protocol · 2025Article
- Article
- Reproducible image-based profiling with Pycytominer.Nature methods · 2025Article
- Drug Repurposing: Insights into Current Advances and Future Applications.Current medicinal chemistry · 2025Review
- Evaluation of image analysis tools for the measurement of cellular morphology.Frontiers in cell and developmental biology · 2025Review
- Article
- Hybrid deep learning technique for COX-2 inhibition bioactivity detection against breast cancer disease.Biomedical engineering letters · 2024Article
- Multiscale engineering of brain organoids for disease modeling.Advanced drug delivery reviews · 2024Review
- Morphological profiling for drug discovery in the era of deep learning.Briefings in bioinformatics · 2024Review
- Improving the power of drug toxicity measurements by quantitative nuclei imaging.Cell death discovery · 2024Article
- AI-organoid integrated systems for biomedical studies and applications.Bioengineering & translational medicine · 2024Review
- High-Content Image-Based Screening and Deep Learning for the Detection of Anti-Inflammatory Drug Leads.Chembiochem : a European journal of chemical biology · 2024Article
- Revealing invisible cell phenotypes with conditional generative modeling.Nature communications · 2023Article
- Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis.Chemical research in toxicology · 2023Article
- Transformative Materials to Create 3D Functional Human Tissue Models In Vitro in a Reproducible Manner.Advanced healthcare materials · 2023Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increase in imaging throughput, new analytical frameworks and high-performance computational resources open new avenues for data-rich phenotypic profiling of small molecules in drug discovery. Image-based profiling assays assessing single-cell phenotypes have been used to explore mechanisms of action, target efficacy and toxicity of small molecules. Technological advances to generate large data sets together with new machine learning approaches for the analysis of high-dimensional profiling data create opportunities to improve many steps in drug discovery. In this review, we will discuss how recent studies applied machine learning approaches in functional profiling workflows with a focus on chemical genetics. While their utility in image-based screening and profiling is predictably evident, examples of novel insights beyond the status quo based on the applications of machine learning approaches are just beginning to emerge. To enable discoveries, future studies also need to develop methodologies that lower the entry barriers to high-throughput profiling experiments by streamlining image-based profiling assays and providing applications for advanced learning technologies such as easy to deploy deep neural networks.
Indexed as
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What OpenQuestion holds
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.