We primarily wrote Autoparty's backend service in Python to handle processing the provided input files, calculating protein-ligand intermolecular interactions, reading and writing to the SQL database, training models, generating model predictions, and selecting examples to service to the frontend. See Supporting Fig. and the Supporting Discussion for the full SQL database schema. All ML models are implemented in the Pytorch deep learning library. The front-end graphical user interface is implemented in JavaScript, HTML, and CSS. The molecular visualization panel uses 3Dmol.js for on-page structure viewing and displaying calculated interactions. The Celery queue and Redis broker 50 handle asynchronous tasks such as updating the database, training models, and creating output prediction summaries. We containerized the application for portability using Singularity (now Apptainer).