Virtual screening of compound libraries via protein-ligand docking serves as a useful approach to navigate the ever-expanding chem. space.Importantly, the quality of hits identified from docking has been shown to improve with the size of the libraries screened.With the advent of ultra-large chem. libraries such as ZINC 20 and Enamine REAL, the amount of data processed during docking experiments is scaling rapidly.Despite the ever-growing scale of docking experiments, existing literature suggests that tools for safely storing and handling docking parameters and results are limited.To address this unmet need, we developed DockDB, a framework that serves as an all-inclusive Relational Database Management System (RDBMS) providing users a computational infrastructure to set up, access, and manage a database dedicated for docking studies.The schema underlying the database encompasses the necessary fields and tables concerning a docking experiment, such as parameters, structures, and files used.DockDB can be integrated with any docking software and automatically updates when results are available.DockDB can either be hosted locally or in the cloud (e.g.AWS).It includes a database version management system that allows users to perform version-controlled changes to the existing schema.User management through roles and groups are predefined, providing supervised access to the database.The infrastructure also includes a stand-alone software package that enables users to ingest, modify, and extract data to and from the database.To demonstrate the utility of DockDB, a large-scale docking experiment was carried out using SMINA across a curated set of crystal structures of human protein kinases.We leveraged DockDB to automate the storage, extraction, and anal. of the docking results.We anticipate that such a database would ensure reproducibility of docking experiments, facilitate the preparation of datasets for training machine learning models, and would serve as an integral component in every virtual screening workflow.