Chemoinformatics Analysis and Structural Similarity Studies of Food-Related Databases

  • Karina Martinez-Mayorga
  • Terry L. Peppard
  • Ariadna I. Ramírez-Hernández
  • Diana E. Terrazas-Álvarez
  • José L. Medina-FrancoEmail author


Chemoinformatics approaches to problem solving are commonly used in both academia and industry, and while a major focus is the pharmaceutical industry, many other sectors of the chemical industry lend themselves to it equally well. The chemoinformatic concepts, thoroughly discussed in Chap. 1 of this book, are general and can also be applied to address problems frequently encountered in food chemistry. A general strategy when applying these computational methods is to replace biological activity by a food-related property, for instance, flavor character or antioxidative activity. In many cases, the representation of the chemical structure remains the same (using, for example, molecular fingerprints, physicochemical and/or structure/substructure representations). In other words, structure/activity relationships (SAR) studies commonly conducted in medicinal chemistry for the purpose of drug discovery can be generalized to the study of structure–property relationships (SPR) for virtually any chemistry-related project. Herein, we discuss representative and specific applications of methods used in chemoinformatics to mine data and characterize SPR information relevant to food chemistry. The chapter is organized into two major sections. First, we discuss exemplary applications of chemoinformatic analyses and characterization of the chemical space of compound databases. In this section, we cover major related concepts such as chemical space and molecular representation. The second section is focused on the application of similarity searching to food chemical databases.


Valproic Acid Quantitative Structure Activity Relationship Virtual Screening Chemical Space Compound Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



K.M-M. thanks the Institute of Chemistry-UNAM and DGAPA-UNAM for funding (PAPIIT IA200513). The authors also wish to thank Robertet Flavors for permission to publish this chapter.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Karina Martinez-Mayorga
    • 1
    • 2
  • Terry L. Peppard
    • 3
  • Ariadna I. Ramírez-Hernández
    • 1
  • Diana E. Terrazas-Álvarez
    • 1
  • José L. Medina-Franco
    • 1
    • 2
    Email author
  1. 1.Departamento de Fisicoquímica, Instituto de QuímicaUniversidad Nacional Autónoma de MéxicoMexico CityUSA
  2. 2.Torrey Pines Institute for Molecular StudiesPort St. LucieUSA
  3. 3.NJUSA

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