ArticleJournal of fluorescence2025
Accelerated Chemical Space Generation of High Molar Extinction Organic Sensitizers via Machine Learning.
Article in Journal of fluorescence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Predicting Stokes Shifts of Fluorescent Chromophores Using Ensemble Learning and H2OAutoML.Journal of fluorescence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The development of organic sensitizers with high molar extinction (ε) coefficients is important for various light absorption applications. To accelerate the discovery of such compounds, a machine learning (ML) analysis has been applied to explore their vast chemical space. A dataset of 676 organic chromophores is analyzed by designing their electronic, topological, and molecular descriptors to predict their ε. Among the 10 tested ML models, Gradient Boosting, Random Forest, Extra Trees, and Historical Gradient Boosting regressors show good correlation with their experimental and predicted values (R
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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.