If the Mordred-calculated features are inter-correlated, "overlap" could cause different features to be identified as the most important depending on the choice of model. A rigorous way to avoid consequences of dependent inputs would be to perform principal component analysis (PCA) on the Mordred features to create linearly-independent features, and train ML models on the principal components. However, PCA inherently results in further loss of interpretability of features used in the model and is therefore outside the scope of this work.