Machine Learning-Based Toxicological Modeling for Screening Environmental Obesogens

The emerging presence of environmental obesogens, chemicals that disrupt energy balance and contribute to adipogenesis and obesity, has become a major public health challenge. Molecular initiating events (MIEs) describe biological outcomes resulting from chemical interactions with biomolecules. Machine learning models based on MIEs can predict complex toxic end points due to chemical exposure and improve the interpretability of models. In this study, a system was constructed that integrated six MIEs associated with adipogenesis and obesity. This system showed high accuracy in external validation, with an area under the receiver operating characteristic curve of 0.78. Molecular hydrophobicity (SlogP_VSA) and direct electrostatic interactions (PEOE_VSA) were identified as the two most critical molecular descriptors representing the obesogenic potential of chemicals. This system was further used to predict the obesogenic effects of chemicals on the candidate list of substances of very high concern (SVHCs). Results from 3T3-L1 adipogenesis assays verified that the system correctly predicted obesogenic or nonobesogenic effects of 10 of the 12 SVHCs tested, and identified four novel potential obesogens, including 2-benzotriazol-2-yl-4,6-ditert-butylphenol (UV-320), 4-(1,1,5-trimethylhexyl)phenol (p262-NP), 2-[4-(1,1,3,3-tetramethylbutyl)phenoxy]ethanol (OP1EO) and endosulfan. These validation data suggest that the screening system has good performance in adipogenic prediction.