The optimized regression model included 8 variables from which two were related to the logP of the chemicals in the training set (Figure ). The most relevant variable was the Crippen logP value explaining around 35% variance of the final model. This logP was calculated based on 68 atomic contributions. On the other hand, the second variable was XLogP, 47 implemented within PubChem. This logP calculation also uses the atomic contribution of 87 groups and additionally incorporates two correction factors, improving its accuracy and expanding its applicability. Another relevant variable for our regression model was the ZMIC1 descriptor which is a 2D descriptor indicating the level of symmetry in the structure. Finally, the remaining relevant descriptors (i.e. excluding logP, XlogP and ZMIC1 descriptors) were related to molecular connectivity, polarizability, and hydrogenbond donation, which all have shown to be relevant in explaining physico-chemical properties and toxicity of chemicals. Performance of Descriptor-Based Direct Classification Model