Wiley Database of Predicted IR Spectra

Technique: IR Spectral Databases

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Discover the power of predicted IR spectra with Wiley

Accelerate Your Analytical Workflow with Expanded Spectral Data Coverage


As a knowledge company and leader in spectral databases, Wiley combines over 60 years of expertise in infrared (IR) spectroscopy and spectral data excellence with the most current machine-learning (ML) techniques to expand the chemical space available to you with the new Wiley Database of Predicted IR Spectra.

This predicted database can be used along with empirical IR spectral reference database in unknown identification to expand your chemical compound coverage and improve your analytical workflow. And because it's built using Wiley’s trusted databases and informatics tools, you can be confident the predicted spectra are based on sound scientific principles and methods.

This ground-breaking predicted IR library:

  • Provides access to an additional 250,000 predicted IR spectra to increase the already large catalog of available Wiley ATR-IR and FT-IR spectra
  • Aids in the analysis of unknowns, especially for rarer compounds and materials, when a match cannot be found in the empirical databases
  • Is useful for functional group classification and is particularly strong with rigid structures
  • Is derived from Wiley’s high-quality, comprehensive empirical reference datasets, including Sadtler spectra
  • Covers of a wide range of compound classes for use across various application areas
  • Includes useful metadata, such as physical properties and chemical structures, along with spectra to narrow your spectral search results
  • Is validated by external and internal SMEs

Just how much can this expand your compound coverage?

As you can see below, the predicted data (blue) fill in the chemical space of the empirical set (red) to provide you with significantly increased compound coverage to enhance your analyses.

Zoomed in 130%, Wiley IR empirical data only.

Zoomed in 130%, computed IR data included.


You can use this collection along with empirical databases to identify, classify, and verify unknown compounds by IR spectroscopy in various applications, such as: environmental, forensics/toxicology, pharmaceutical, biotech, automotive/aerospace, food/cosmetics, and many more.

Trusted Data from a Trusted Source

Wiley is an authoritative source for spectral data. Their renowned Sadtler databases were processed according to rigorous protocols to ensure they are of the highest quality. These qualification procedures start at data acquisition and continue throughout the database development process. Any data acquired from trusted partners is thoroughly vetted before inclusion in our collections.

KnowItAll is a trademark of John Wiley & Sons, Inc. in certain jurisdictions.
Subscription content is subject to change as data may be added or deleted from collections periodically.

Compound coverage

Covers a range of chemical compound classes, including:

  • Organics
  • Flavors and Fragrances
  • Industrial compounds
  • Androstanes
  • Estrogens and steroids
  • Geochemicals
  • Petrochemicals
  • Biomarkers
  • Drugs
  • Pesticides
  • Toxicology
  • Volatiles in Food
  • Natural Products
  • Metabolites
  • Lipids
  • Monomers
  • PFAS

Additional information

When it comes to spectral analysis, the more data you have the better. This predicted library contains the following valuable metadata to help you narrow and refine your search results:

  • Chemical Structure
  • Chemical Name
  • Exact Mass
  • Formula
  • InChI/InChIKey
  • Molecular Weight
  • Nominal mass
  • Spectrum/Structure Validation Score

External & internal validation and usage recommendations

To generate this database of computed spectra, Wiley Science Solutions used an AI-powered spectrum prediction engine based on its high-quality Fourier-transform infrared spectroscopy (FTIR) empirical spectral databases—the largest collection commercially available.

Wiley then conducted both external and internal studies by SMEs to validate results from the predicted library. From these two studies, the SMEs concluded that the library characterizes unknown spectral functional groups and performs well when searched against with sample spectra. SMEs suggest optimal workflow for the computed library is to use it when the empirical library results in either low hit quality index (HQI) scores, poor matching, or no matches to help users classify and determine the structural characteristics of unknown compounds.

While Wiley has the largest commercially available library of extensive and high-quality empirical datasets, the total coverage is significantly smaller than the overall chemical space in use by chemists, life scientists, and materials scientists – i.e., the total number of possible molecules and compounds within a set of elements and rules. Augmenting our empirical coverage within the bounds of a predictive model (the chemical space of the underlying training set) is a strategy to help improve overall coverage for unknown identification, especially for rarer compounds and materials.

Predicted libraries, particularly when constructed from extensive and high-quality empirical reference datasets such as Wiley’s extensive spectral collections (including premium Sadtler spectra), demonstrate performance levels closely approaching that of empirical datasets. Consequently, they can be effectively integrated into the analytical qualification workflow.

See Validation Study


Your peers trust predicted data from trusted sources. In a recent survey Wiley conducted, over 80% indicated they are likely to use predicted databases that are validated by a reputable source for spectral analysis.


Ordering information

Product Code



Wiley Database of Predicted IR Spectra (Annual Subscription)


Wiley Database of Predicted IR Spectra (Annual Subscription Renewal)


    • Recommended as a supplement to the KnowItAll IR Spectral Library collection of curated empirical data.
    • For use with KnowItAll 2024 software.
    • KnowItAll software is compatible with most major instrument formats. See supported KnowItAll file formats.