The dataset used for our model development, validation, and testing consists of calculated descriptors, monoisotopic mass of each chemical, and experimentally determined LC50 values (96 hours) for acute fish toxicity (see details in Section Dataset). The LC50 values were divided into four categories namely: very low toxicity, low toxicity, moderate toxicity, and high toxicity via k-means clustering. This categorization followed the typical evidence-based effect modeling categorization. Additionally, regulatory-defined toxicity categories were retrieved from the Globally Harmonized System of Classification and Labelling of Chemicals (GHS). We assessed the prediction accuracy of the two types of toxicity categories by employing two different modeling strategies: a conventional QSAR regression model vs direct classification (Figure . The QSAR regression model simulated the case where the acute fish toxicity (as LC50) is predicted based on molecular descriptors via a QSAR model and then the chemical is assigned a specific toxicity category in a separate step. On the other hand, the direct classification model skipped the LC50 prediction step and directly classified the chemical of interest into one of the initially defined toxicity categories. This comparison was performed for the full dataset (i.e. training set and test set) in order to assess the accuracy of each approach in acute fish toxicity categorization.