as one of the main causes of attrition during clinical trials. Thus, the number of possible causes for a compound to fail or to have barely tolerable adverse effects is quite large. Moreover, in vitro and in vivo characterization of a compound's properties can become very costly and time-consuming. For all of these reasons, considerable effort has been made to develop computational models for predicting ADME-Tox properties . AI models have leveraged the information available in heterogeneous ADME-Tox data sets and helped to improve the accuracy of early drug efficacy and safety predictions.