FADB-China: A molecular-level food adulteration database in China based on molecular fingerprints and similarity algorithms prediction expansion

Abstract

Food adulteration is a growing concern worldwide. The collation and analysis of food adulteration cases is of immense significance for food safety regulation and research. We collected 961 cases of food adulteration between 1998 and 2019 from the literature reports and announcements released by the Chinese government. Critical molecules were manually annotated in food adulteration substances as determined by food chemists, to build the first food adulteration database in China (http://www.rxnfinder.org/FADB-China/). This database is also the first molecular-level food adulteration database worldwide. Additionally, we herein propose an in silico method for predicting potentially illegal food additives on the basis of molecular fingerprints and similarity algorithms. Using this algorithm, we predict 1919 chemicals that may be illegally added to food; these predictions can effectively assist in the discovery and prevention of emerging food adulteration.