ArticleRSC advances2025
DyeLeS: a web platform for predicting and classifying fluorescence properties of bioactive molecules.
Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
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Who cites it
2 citing papers in PubMed.
- New benzoxanthenone and benzanthrone derivatives from Tricholaena teneriffae L. roots: structure elucidation, α-amylase inhibitory activity and molecular docking studies.BMC chemistry · 2026Article
- Exploration of a Novel Thiadiazole Derivative: Design, Synthesis, Biological Evaluation (In Vitro and in Silico), and DFT Studies.Journal of fluorescence · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fluorescent drug molecules play a pivotal role in biomedical research and precision medicine. Their intrinsic fluorescence enables real-time tracking of drug distribution, target engagement, and metabolic pathways, while avoiding interference from external labeling. However, traditional fluorescent drug discovery relies heavily on trial-and-error approaches, which are inefficient and resource-intensive. To address this, we developed DyeLeS (Dye-Likeness Scoring), a web platform designed to rapidly evaluate molecular fluorescence potential and predict key photophysical properties such as Stokes shift and quantum yield. DyeLeS utilizes a curated dataset of fluorescent and non-fluorescent compounds, applies a Naive Bayes-inspired algorithm for fluorescence classification (AUC = 0.995), and employs a LightGBM model for quantitative prediction of fluorescence properties, achieving an
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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.