Before revision:  Therefore, MoleculeKit not only serves as a comprehensive tool, but also contributes towards developing novel and advanced graph and sequence learning methodologies.
After revision:  In order to learn effectively from highly imbalanced datasets, we develop advanced loss functions that optimize areas under precision-recall curves. Altogether, our work not only serves as a comprehensive tool, but also contributes towards developing novel and advanced graph and sequence learning methodologies.