The user can then grade these molecules. These assigned grades are saved to an SQL database to maintain a robust record of structures and human annotations. During this human-in-the-loop training, a machine learning (ML) model is trained to predict annotations for new molecules and determine which molecules are the most informative for the user to inspect manually. This Autoparty calculates each protein-ligand complex's interactions and LUNA interaction fingerprints (IFPs) upon upload. The back end also saves the user-provided grades, trains models to predict human labels, and orders compounds by the acquisition function.