DyeLeS: a web platform for predicting and classifying fluorescence properties of bioactive molecules

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 R ² of 0.88 in absorption wavelength (λ _abs) prediction. Leveraging DyeLeS, this study constructed FluoBioDB, the first publicly available library of fluorescent bioactive compounds, encompassing 32 865 structurally diverse molecules, including kinase inhibitors and GPCR modulators. Case analyses indicate that FluoBioDB compounds typically possess polycyclic conjugated frameworks, donor-acceptor (D-A) structures, and rigid planar cores, endowing them with strong potential for applications in bioimaging, targeted therapy, and theranostics. This work presents a robust computational framework and a valuable molecular resource to facilitate the rapid discovery and optimization of fluorescent drug candidates. All source codes and datasets are available at https://github.com/MolAstra/DyeLeS, and the web server can be accessed at https://dyeles.molastra.com.