Predicting chemicals' toxicity pathway of female reproductive disorders using AOP7 and deep neural networks

Abstract

Experimental evidence shows that certain chemicals, particularly endocrine disrupting chemicals, may negatively affect the female reproductive system, thereby lowering women's fertility. However, humans are constantly exposed to a number of different chemicals with limited or no experimental data regarding their effect and the mechanism of action in the female reproductive system. To predict chemical hazards to the female reproductive system, we used a previously defined adverse outcome pathway (AOP) that links activation of the peroxisome proliferator-activated receptor γ to the reproductive toxicity in adult females (AOP7) and the Convolutional Deep Neural Network models that produce meaningful predictions when trained on a significant amount of data. The models trained using CompTox assays with intended molecular and biological targets corresponding to AOP7 achieved high performance (over 90% validation accuracy). The integration of AOP7 and Deep Neural Network identified chemicals that could negatively affect female reproduction through the mechanism described in AOP7. We provide a solution to quickly analyze the data and produce machine learning models to identify potentially active chemicals in the female reproductive system. Although we focused on the female reproductive system, this approach could be valid for a number of other chemicals and AOPs if the right data exist.

Keywords: Adverse outcome pathway; Artificial intelligence; CompTox; Female reproductive system; Machine learning; Toxicity.