The "Performance" tab lists various performance measures of the QSAR model obtained by cross-validation or on an independent hold out test set (Figure ). The validation procedure can be adjusted by the user during model creation (Figure ). Making predictions with the model is possible under the "Predictions" tab. Each QSAR model can be used to make predictions for any compound set listed on the "Compounds" page and the calculated predictions will then become visible in that interface as well (Figure ). New QSAR models are submitted for training with a creation card (Figure ) that helps users choose model hyperparameters and a suitable training strategy (i.e. the characteristics of the independent hold out validation set, the number of cross-validation folds or the choice of validation metrics). The "Info" tab of a trained model contains important metadata as well as a hyperlink to export the model and save it as a reusable Python object. This import/export feature enables users to archive and share their work, enhancing the reusability and reproducibility of the developed models . The "Performance" tab can be used to observe model performance data according to the chosen validation scheme (Figure ). This information is different depending on the chosen model type (regression vs. classification, Figure vs. Figure ) and the parameters used (i.e. the choice of validation metrics).