Fluorescence Spectrum Prediction Service

At CD ComputaBio, we specialize in advanced computational biology solutions that empower researchers across various fields. Our Fluorescence Spectrum Prediction Service leverages cutting-edge computer simulations to provide accurate, reliable, and insightful predictions of fluorescence spectra. This service is designed for scientists, researchers, and industries involved in areas such as drug discovery, environmental monitoring, and materials science.

Fig 1. Diagram showing the process by which fluorescence is produced. (Lathey D C,2005)

Why Fluorescence Spectrum Prediction is Important?

Identifying Molecular Features - Understanding the unique spectral signatures of compounds.
Optimizing Experimental Conditions - Tailoring experiments to achieve desired spectral responses.
Streamlining Research - Pre-screening potential compounds to reduce experimental workloads.
Enhancing Data Analysis - Providing theoretical frameworks for interpreting complex spectral data.

Our Services

Fluorescence Spectrum Predictions

Single Compound Prediction - For individual compounds, we provide a detailed fluorescence spectrum prediction based on structural and electronic properties.
Mixture Analysis - We can predict the fluorescence spectra of complex mixtures, providing insights into interactions between different fluorescent species.

Fluorescence spectrum

Spectral Data Analysis

Data Comparison - We identify discrepancies and provide insights into the underlying causes by comparing predicted spectra with experimental results.
Stability Evaluation - Assessing the stability of the fluorescence signal under different conditions.

Data analysis

Custom Fluorescent Compound Modeling

We offer tailored modeling services for novel fluorescent compounds, ensuring precise simulation of their spectral behavior under various experimental conditions. Our team employs sophisticated quantum mechanical calculations to predict emission wavelengths, intensities, and quantum yields.

Compound modeling

Analysis Methods

Quantum Mechanics (QM)

Quantum mechanics forms the backbone of our fluorescence spectrum predictions. We use methods like Hartree-Fock (HF) and Density Functional Theory (DFT) to calculate the electronic transitions and energy levels of molecules. This allows us to predict the wavelengths and intensities of fluorescence emissions accurately.

Molecular Dynamics (MD)

To capture the dynamic behavior of molecules in varying environments, we employ molecular dynamics simulations. These allow us to observe the conformational changes that may occur during excitation and emission processes, providing a more realistic prediction of fluorescence spectra.

Machine Learning Techniques

As an innovative approach, we also integrate machine learning algorithms to enhance the accuracy of our predictions. By training models on extensive datasets of known fluorescence spectra, we can predict the spectra of new compounds with remarkable precision.

Robust Computational Algorithms

Time-Dependent Density Functional Theory (TDDFT)
For calculating electronic transitions and predicting emission spectra.
Quantum Mechanics/Molecular Mechanics (QM/MM)
For systems where both quantum and classical molecular interactions are significant.

Sample Requirements

Chemical Structure	Please provide the chemical structure in either SMILES format or as a 2D/3D molecular file (e.g., sdf, mol, pdb).
Experimental Conditions	Include details about the solvent used for your experiments, as solvent polarity and properties significantly influence fluorescence.
Background Data	Providing previous experimental spectra or data your compound has generated can improve the prediction accuracy if available. This may include: Experimental fluorescence spectra. Absorption spectra.

References:

Lathey D C. Fluorescence prediction through computational chemistry[M].Marshall University, 2005.
Ye Z R, Huang I S, Chan Y T, et al. Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach[J]. RSC advances, 2020, 10(40): 23834-23841.

Services

Related Services: