Computational methods on food contact chemicals: Big data and in silico screening on nuclear receptors family

Abstract

According to Eurostat, the EU production of chemicals hazardous to health reached 211 million tonnes in 2019. Thus, the possibility that some of these chemical compounds interact negatively with the human endocrine system has received, especially in the last decade, considerable attention from the scientific community. It is obvious that given the large number of chemical compounds it is impossible to use in vitro/in vivo tests for identifying all the possible toxic interactions of these chemicals and their metabolites. In addition, the poor availability of highly curated databases from which to retrieve and download the chemical, structure, and regulative information about all food contact chemicals has delayed the application of in silico methods. To overcome these problems, in this study we use robust computational approaches, based on a combination of highly curated databases and molecular docking, in order to screen all food contact chemicals against the nuclear receptor family in a cost and time-effective manner.

Keywords: Computational chemistry; Consensus prediction; Database; Nuclear receptors; Toxicology.