The data was introduced into Logistic Regression, Random Forest, Gradient Boosting, k-nearest neighbours, Support Vector Classification, XGBoost and LightGBM algorithms. Models were checked in five-fold cross validation process. Oversampling, standardization and PCA were applied separately for every training fold of the data in cross validation. The validation part of the cross validation data fold underwent only standardization and PCA with parameters previously set on the training part of the data fold, thus the scoring metrics were evaluated with data with the original class ratio. The testing metrics used to evaluate models were F-score and balanced accuracy. Following good practices in machine learning studies , in order to check whether the predictions are better than simple guesses, a random classifier was set as a baseline. With the aforementioned scoring results in mind one, best performing algorithm was selected. The algorithm's parameters were optimised in order to maximize model's accuracy. The process was conducted in 5-fold cross validation. The selected algorithm with parameters tuned was trained with the whole training dataset and finally tested with OOB dataset. Prior to the final test, the training dataset was oversampled, standardized and undergone PCA. The test dataset was standardized and PCA was applied. The final model was tested 5 times with different seeds of pseudorandom generators of the machine learning algorithms. The diagram depicting the whole process is shown in Figure . In order to create a full picture of the potential of the novel molecular descriptors, the research process was repeated with 2 other sets of molecular descriptors: