A Systematic Analysis and Prediction of the Target Space of Bioactive Food Compounds: Filling the Chemobiological Gaps

Abstract

Food compounds and their molecular interactions are crucial for health and provide new chemotypes and targets for drug and nutraceutic design. Here we retrieve and analyze the complete set of published interactions of food compounds with human proteins, using the FooDB as compound set and ChEMBL as source of interactions. The data is analyzed in terms of 19 target classes and 19 compound classes, showing a small fraction of target assignment of the compounds (1.6%) and unraveling multiple gaps in the chemobiological space for these molecules. By using well established cheminformatic approaches (Similarity Ensemble Approach (SEA) combined with the maximum Tanimoto Coefficient to the nearest bioactive, “SEA + TC”) we achieve a much enhanced target assignment (64.2%), filling many of the gaps with target hypothesis for fast focused testing. By publishing these datasets and analyses we expect to provide a set of resources to speed up the full clarification of the chemobiological space of food compounds, opening new opportunities for drug and nutraceutic design.