The optimized direct classification model resulted in a classification accuracy of 92% for the training set and around 80% for both the cross-validation and the test set, for the four k-means categories. The final model used 230 variables out of a total of 2036 curated descriptors. Similar to the regression model, most of the important variables were a combination of 2D descriptors and fingerprints (i.e. 3D ) (Figure ). These descriptors included the four logP calculations (e.g. CrippenlogP) as well as parameters related to polarizability and charge distribution. These parameters are all highly relevant to the mobility of the chemicals and their binding potential with the active sites. Differently from the regression model, the most relevant variable only explained ≈ 1.5% of variance (vs 35% for the regression model) in the final model. Even though larger number of variables were included in the model, the total number of variables were less than 30% of the number of measurements resulting in a mathematically well-defined problem. Additionally, a larger number of variables enables a better assessment of the model applicability domain. The direct classification model based on the three GHS categories, resulted in an accuracy of 94% for the training set and around 85% for the cross-validation and test set. This model, similar to the previous one, had 236 high importance variables that were included in the final model. The high importance variables (e.g. top 20) for both models were exactly the same as for the direct classification into the k-means categories with similar levels of variance explained.