For the QSAR regression model, we observed 178 cases of wrong classifications for k-means based categories whereas 163 incorrectly classified cases were observed for the GHS cate-gories (Figure ). The direct classification strategy showed a homogeneous distribution of the miscategorized chemicals in the acute fish toxicity dataset, for both the k-means and the GHS categories. For the k-means categorization the QSAR regression model resulted in large and homogenous distribution of wrong categorization while for the GHS approach we observed a high density of miscategorization for high and moderate toxicity groups, (Figure ). Around 85% of the miscategorized chemicals via direct classification overlapped with those wrongly categorized via the QSAR regression model, independently of the type of categories. For example a chemical that was consistently wrongly categorized by all the methods was 1-hydroxypyridine-2-thione (InChyKey:YBBJKCMMCRQZMA-UHFFFAOYSA-N) with measured LC50 of 0.95 µg/L (i.e. -3.02 log(mg/L)). This chemical was categorized as moderately toxic by both models while it is actually a high toxicity chemical. When looking at the structure of this chemical, it is clear that this chemical is not very well covered by our training set. In other words, there are not enough (at least 4) chemicals with a similar structure to this one in our training set. This further indicates that the addition of more diverse chemical structures to our training set will result in even more accurate prediction of the toxicity categories. Additionally, the replacement of the molecular descriptors with the topographical fingerprints, given their stability, may further improve our prediction accuracy.