ReviewMolecules (Basel, Switzerland)2026
Artificial Intelligence-Powered High-Content Analysis: Methodologies and Applications in Bioactive Compound Discovery from Natural Sources.
Review in Molecules (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
High-content screening (HCS) is a useful phenotypic drug discovery technology that combines automated microscopy, image analysis, and high-throughput experimentation to comprehensively characterize biological responses to diverse perturbations. This review summarizes the methodological fundamentals of high-content analysis, including image preprocessing, cell segmentation, feature processing, and downstream analysis, as well as the diverse phenotypic datasets generated from different biological models, perturbation strategies, and staining approaches. Recent advances in artificial intelligence, particularly deep learning, have improved cell segmentation, image representation learning, and phenotypic profiling, enabling more accurate and scalable analysis of HCS data. We further highlight emerging applications of AI-powered HCS in pharmaceutical research, with a particular focus on the discovery of bioactive compounds from natural sources. Finally, we discuss current challenges and future perspectives, including the construction of large-scale phenotypic databases, the integration of AI throughout the screening workflow, and the development of intelligent screening platforms. These advances are expected to accelerate phenotype-driven drug discovery and promote innovation in natural product research.
Indexed as
Identifiers
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.