Abstract
The exposome encompasses a vast chemical space that can originate from the consumer industry and environmental sources. Once these chemicals enter into cells (human or other organisms), they can be also transformed into products that differ in terms of toxicity and health effects. Recent developments in machine learning methods and chemical data science resources have enabled the in-silico prediction of transformation reactions. Here, we report an integrated workflow of these existing resources (RXNMmapper, Rxn-INSIGHT and RDChiral) to predict the transformation product for any chemical in the exposome. We have generated a large library of reaction templates from > 90,000 reactions sourced from the PubChem database. Utility of the reaction screening and transformation prediction workflow has been demonstrated for insecticides structures (n=181), yielding 21,284 unique transformation products. Many of these products have PubChem entries but have not yet been linked with the parent compounds. By identifying a multitude of plausible new products, this approach may expand our understanding of the metabolism of chemical exposure, guide future biomonitoring research and characterize of the human exposome in the existing datasets and chemical databases.