Chemometric methods, structure-activity relationships (SAR), molecular modeling, and quantitative structureactivity relationships (QSAR) are some of the in silico methods widely used in medicinal chemistry, and that are found to be appropriate to be employed in food research. Some of these methodologies represent an attempt to correlate structural or property descriptors of compounds to several types of biological activities. The physicochemical traditional descriptors include parameters related to hydrophobicity, topology, electronic properties, and steric effects. Other computational tools, such as density functional theory (DFT) calculations, are used in order to examine the influence of the electronic surfaces of new ingredients. Advances in the development of in silico methods, such as structure similarity searching, have increased the availability of additional resources for safety assessment. This review analyzes some studies in order to show how computational chemistry can be used to predict important chemical structure information and how these techniques can be applied in food research.
Keywords: Computational methods, Food Chemistry, Mutagenic Compounds, SAR, QSAR:, pharmacological activity, PCA, PLS regression, DFT, Food Ingredients
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