The choice of models, in this case, study was motivated by the recently added multi-task capability to the popular xgboost package . Since the models implemented in this package adhere to the scikit-learn API, they can be readily used in QSPRpred with the SklearnModel class. For the multi-task scenario, we compared the two modelling strategies currently implemented by xgboost: (1) Multi Output Tree (MOT) and (2) One Output Per Tree (OOPT).