Quantitative structure-activity relationship (QSAR) models have long been used to predict pharmacological promiscuity and toxicity ( ). These models show that the biological characteristics of these substances are related to different functional groupings. The spread of toxicity databases in the current setting has helped to adopt machine learning (ML) algorithms to predict the toxicity of small molecules. As such, these models have become somewhat popular recently. Consequently, the scholarly literature has recorded many toxicity prediction methods based on machine learning, including MolToxPred ( ) and DeepTox ( ), a neural network model. Although the examples demonstrate the ML models provide promising performance in toxicity predictions, they still have limitations, such as low interpretability.