The final k-means model resulted in a clustering accuracy of 97.5%. This model, then, was fed the full acute fish toxicity dataset to define the toxicity category of each chemical in that dataset. The final model was saved as a binary file to be used for prediction (Figure ). The k-means and GHS categories were used as labels in two separate runs of the direct classification model while the 96h LC50 values for acute fish toxicity predicted by the QSAR regression model were converted into the two types of acute toxicity categories in a second step. When comparing the unsupervised k-means clustering-based categorization with the expert knowledge based categorization from the GHS, we see a high level of similarity in the thresholds (Figure ). In fact the main differences were observed for chemicals with a molecular weight of ≥ 400 Da and LC50 values ≥ 1 mg/L (0 log(mg/L)). These chemicals in the k-means categorization were considered part of the high toxicity category while based on the GHS categories they were considered moderate to low toxicity. When calculating the similarity scores between the descriptors of those chemicals and the two categories, we consistently observed higher values for high toxicity category. This indicates that those chemicals may be structurally more similar to the high toxicity category rather than the moderate and/or low one. These similarities are better captured by the k-means model, given that it uses two variables (96h LC50 and monoisotopic mass) and Euclidean distances for the cluster creation.