Due to the possibility that some fragment quantum descriptor values may be missing, three types of handling strategies were evaluated using 5-fold cross validation. Leaving missing values, filling with zeros while adding additional variables containing information on whether the value was missing, and k-nearest neighbours imputation were the strategies evaluated. The bio-concentration classes were encoded with the values 0 and 1, with 1 denoting bioaccumulation. 330 molecules were sampled while maintaining the classes ratio. This procedure generated an out-of-the-bag (OOB) dataset for testing the final model. The remaining samples formed a training set. Since the data is class-imbalanced, random oversampling was used to modify the class distribution in the training set. The training set's values were then subject to standardisation.