Based on the experimental databases, a random forest implementation from scikit-learn Python module with 500 estimators and max features parameters set to 0.3 and defined random seed was used to model the experimental properties or activities. 10 times repeated ten-fold cross-validation was applied to assess the models' predictive performance. The performance was evaluated by a set of metrics: in the case of regression, the Pearson's coefficient R 2 and root mean squared error (RMSE). The classification performance metrics were balanced accuracy and area under receiver operating curve (ROC AUC).