embryoTox: using graph-based signatures to predict small molecule teratogenicity

Abstract: Teratogenic drugs can lead to extreme foetal malformation and consequently critically influence the foetus's health, yet the teratogenic risks associated with most approved drugs are unknown. Here, we propose a novel predictive tool, embryoTox, which utilises a graph-based signature representation of the chemical structure of a small molecule to predict and classify molecules likely to be safe during pregnancy.

embryoTox was trained and validated using in vitro bioactivity data of over 700 small molecules with teratogenicity effects. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.96 on 10-fold cross-validation and 0.82 on independent non-redundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a practical resource for optimising screening libraries to determine effective and safe molecules to use during pregnancy.To provide a simple and integrated platform to rapidly screen for potential safe molecules and their risk factors.