In contrast, the usefulness of rule-based methods lies in their interpretability, linear scaling with dataset size, and dataset-independence (meaning that the result of a class assignment for a given compound is independent from the overall composition of the dataset) . As the name suggests, rule-based algorithms rely on the chemical structure and apply rules to determine the class of a given compound. For example, compounds can be grouped together based on the commonality of their molecular frameworks, a concept introduced by Bemis and Murcko .