Spectrum Prediction Service
Spectrum prediction plays a crucial role in modern chemical and pharmaceutical research, enabling scientists to analyze molecular properties, verify structural assignments, and accelerate drug discovery. CD ComputaBio's spectrum prediction service leverages advanced spectrum prediction methods to deliver accurate, reliable, and cost-effective spectral simulations for a wide range of applications.
Introduction to Spectrum Prediction
Spectrum prediction are fundamental tools in chemistry, biochemistry, and materials science for characterizing molecular structures and interactions. Methods such as nuclear magnetic resonance (NMR), infrared (IR), ultraviolet-visible (UV-Vis), and mass spectrometry (MS) provide critical insights into molecular composition, conformation, and electronic properties. However, obtaining experimental spectral data can be a time-consuming and costly process, requiring specialized instrumentation and sample preparation.
Figure 1. Computational Spectrum Prediction. (Park J, et al., 2024)
Computational Spectrum Prediction
Computational spectrum prediction involves predictions based on molecular structure. By applying quantum mechanical calculations and machine learning algorithms, theoretical spectra can be generated that match experimental observations. This approach is valuable for:
- Validating molecular structures
- Reducing reliance on time-consuming experimental measurements
- Interpreting complex spectral data
- Supporting research in drug development, material science, and synthetic chemistry
Tools for Spectrum Prediction
Advanced software and algorithms are employed to ensure high accuracy, including:
- Gaussian, ORCA, Turbomole (for quantum chemical calculations)
- NWChem, Dalton (for specialized spectroscopic simulations)
- Custom machine learning models (for high-throughput predictions)
Our Services
The spectrum prediction service is designed to predict various spectroscopic properties of molecules, including NMR, IR, UV-Vis, and Raman spectra. By simulating how molecules interact with electromagnetic radiation, CD ComputaBio enable researchers to correlate computational predictions with experimental data. This predictive capability is invaluable for characterizing new compounds and elucidating molecular structures.
By Types
By Methods
By Workflow
Input Submission
Molecular structures are provided in standard formats (SMILES, SDF, PDB).
Computational Setup
Appropriate methods and basis sets are selected based on the target spectrum.
Calculation & Optimization
Quantum chemical or machine learning models generate spectral data.
Analysis & Validation
Predicted spectra are compared with experimental references (if available).
Report Delivery
Detailed results, including spectral plots and interpretation, are provided.
Our Advantages
Advanced Algorithms
Use the latest artificial intelligence and machine learning methods to improve the accuracy of spectral predictions and ensure the reliability of data.
Interdisciplinary Team
Bring together experts from different disciplines to conduct data analysis and technology development from multiple perspectives to ensure the comprehensiveness and foresight of services.
Flexible service Model
According to the needs of different customers, we can provide one-time services or long-term cooperation, and flexibly adjust the service content and depth.
CD ComputaBio's spectrum prediction service provides researchers with powerful computational tools to simulate and analyze spectroscopic data across multiple domains. By integrating quantum chemistry, machine learning, and expert validation, the service delivers precise predictions that enhance structural characterization and accelerate scientific discovery. If you are interested in our services or have any questions, please feel free to contact us.
Reference:
- Park J, Jo J, Yoon S. Mass spectra prediction with structural motif-based graph neural networks. Scientific Reports, 2024, 14(1): 1400.
* For Research Use Only.
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