FLSF — The World’s Most Advanced and Comprehensive AI Prediction Tool Is Now Live!
Based on a breakthrough study published in Nature Communications (2025)
Lowest MAE among six benchmark models | Dataset of 109,000+ entries — the world’s largest
💡 Revolutionizing Fluorescence Research with AI
In cutting-edge fields such as organic optoelectronics, bioimaging, and fluorescence sensing, designing high-performance fluorescent molecules remains a key challenge.
Traditional trial-and-error approaches are time-consuming, while theoretical calculations like TD-DFT, though accurate, are computationally expensive — limiting their use in high-throughput screening.
Now, that challenge is being transformed.
We are proud to introduce FLSF (Fluorescence Learning Structure-Feature Model) — a deep learning–based platform for predicting fluorescence photophysical properties, now officially launched and open for use.
Developed from the breakthrough research published in Nature Communications (2025, DOI: 10.1038/s41467-025-58881-5), FLSF is currently the most accurate, comprehensive, and powerful AI prediction tool for fluorescence — designed for experimental chemists, materials scientists, and computational researchers alike.
🔍 Six Reasons Why FLSF Is the Ultimate Choice
✅ 1. Industry-Leading Accuracy Across Six Benchmark Models
The study systematically evaluated six state-of-the-art models — Random Forest, SVR, GCN, MPNN, Transformer, and FLSF.
FLSF achieved the lowest mean absolute error (MAE) for absorption wavelength (λ_abs), emission wavelength (λ_em), and photoluminescence quantum yield (PLQY), outperforming all others.
FLSF currently holds the highest predictive accuracy among all published fluorescence AI models.
✅ 2. Trained on the World’s Largest Fluorescence Dataset — 109,000+ Records
FLSF is powered by FluoDB, the most comprehensive fluorescence database ever built, integrating both public and proprietary data:
109,054 optical property records (absorption, emission, PLQY, energy gap, etc.)
35,528 unique fluorescent molecules
55,169 molecule–solvent combinations
Covers 16 major fluorescent skeletons: carbazole, coumarin, anthracene, pyrene, BODIPY, rhodamine, cyanine, phenoxazine, and more
Data is the fuel of AI — and FLSF runs on the richest data foundation available.
✅ 3. Dual-Input Model: Molecule + Solvent
Unlike traditional models that ignore solvent effects, FLSF uniquely integrates both molecular SMILES and solvent information as dual inputs.
This allows accurate modeling of solvent polarity, hydrogen bonding, and other environmental influences.
Whether you work in DMSO, acetonitrile, chloroform, or water, FLSF predicts results that better reflect real experimental conditions.
✅ 4. Instant Prediction of Four Key Photophysical Parameters
Simply input the molecular SMILES and solvent to instantly obtain:
Parameter Description
abs_pred Absorption wavelength (nm)
emi_pred Emission wavelength (nm)
plqy_pred Photoluminescence quantum yield (Φ_PL)
e_pred Energy gap (eV)
No complex computations required — results are generated in as little as 3 seconds, enabling rapid virtual screening of large molecular libraries.
✅ 5. User-Friendly Web Interface — Zero Coding Required
The FLSF Web Tool is designed for both experimental and computational researchers:
Enter molecular SMILES (e.g., O=C1OC2=CC(N)=CC=C2C3=C1N=CO3)
Select or input the solvent (supports names and SMILES, in English or Chinese)
Click “Start AI Prediction”
Instantly view the results
No installation, no scripting — just pure, accessible AI power.
✅ 6. Open Science: Transparent, Reproducible, and Trustworthy
Full model architecture and training details are published in Nature Communications
Partial release of FluoDB (DOI: 10.1038/s41467-025-58881-5)
Source code available on GitHub
FLSF is not just a tool — it’s an open platform driving the future of AI-powered fluorescence research.
🧪 Research Case Study: Accelerating Fluorescent Molecule Design
Case Study: AI-Guided Design and Experimental Validation of Novel Fluorophores
(Nature Communications, 2025)
A research team used FLSF to design oxazole-fused coumarin derivatives through a complete AI–experiment feedback loop:
AI Prediction: FLSF predicted absorption, emission, and PLQY for candidate molecules.
Screening: Three high-potential structures were selected.
Experimental Validation: Synthesized and tested results matched predictions with remarkable accuracy —
deviations <15 nm for λ_abs/λ_em and <0.1 for PLQY.
This demonstrated FLSF’s predictive reliability and its potential to revolutionize molecular discovery.
🚀 Experience FLSF Now — Enter the Era of Intelligent Fluorescent Design
🔗 Try it here: https://www.omichem.com/en/tools/flsf-en-v5
📚 Reference: Nature Communications (2025), 16, 2345
👩🔬 Who Should Use FLSF?
Researchers in organic light-emitting materials, OLEDs, fluorescent probes, and bioimaging
Experimental chemists seeking faster molecular screening and reduced trial-and-error
Computational scientists exploring AI for Science and molecular property prediction
📧 Contact: [email protected]
