ProDock Docking Workflow Overview
Figure 1: Overview of the ProDock workflow. The package organizes docking into four connected stages: preprocessing of receptor and ligand inputs, docking execution, postprocessing of poses and interaction fingerprints, and SQLite result repository for downstream querying and comparison.
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Paper title: ProDock: From multi-target consensus docking into database-backed storage Abstract: Protein--ligand docking is widely used in structure-based discovery, but routine studies often fail at the workflow level rather than at the scoring level. Receptor cleaning, ligand preparation, file conversion, box definition, run organization, and downstream parsing are frequently handled by fragmented scripts, which reduces reproducibility, obscures provenance, and complicates comparative analysis across targets, ligands, and docking settings. We present ProDock, an open-source Python toolkit for reproducible protein--ligand docking and postprocessing. ProDock organizes application-oriented docking into four connected layers: receptor and ligand preprocessing, provenance-aware docking execution, postprocessing of poses and interaction fingerprints, and SQLite-backed storage for later querying. The package supports inputs ranging from PDB identifiers and local receptor files to \texttt{SMILES} strings and prepared ligand directories, and integrates receptor preparation, ligand preparation, reference-ligand-based box generation, campaign serialization, batch docking, pose crawling, score extraction, interaction profiling, and database insertion within a consistent project-local workflow. By representing studies as explicit many-to-many campaigns linking multiple receptors, ligands, and docking backends, ProDock converts fragmented engine-specific outputs into structured analytical results that are easier to compare, reuse, and audit. ProDock is implemented in Python and rele Passages referencing this figure: roDock reduces the practical overhead that often limits replication of docking workflows across datasets and collaborators. The software is distributed through PyPI and conda , with installation and dependency details described in Supporting Section S1 . 2 Implementation ProDock is organized into a four-stage workflow, including preprocessing , docking , postprocessing , and database storage (see Figure 1 ), and its architecture is divided into five subpackages, detailed in Supporting Section S1 . Figure 1: Overview of the ProDock workflow. The package organizes docking into four connected stages: preprocessing of receptor and ligand inputs, docking execution, postprocessing of poses and interaction fingerprints, and SQLite result repository for downstream querying and comparison. 2.1 Prep s and collaborators. The software is distributed through PyPI and conda , with installation and dependency details described in Supporting Section S1 . 2 Implementation ProDock is organized into a four-stage workflow, including preprocessing , docking , postprocessing , and database storage (see Figure 1 ), and its architecture is divided into five subpackages, detailed in Supporting Section S1 . Figure 1: Overview of the ProDock workflow. The package organizes docking into four connected stages: preprocessing of receptor and ligand inputs, docking execution, postprocessing of poses and interaction fingerprints, and SQLite result repository for downstream querying and comparison. 2.1 Preprocessing of receptors and ligands The workflow begins with preprocessing (see Figure 1 A), where raw r Supporting Section S1 . Figure 1: Overview of the ProDock workflow. The package organizes docking into four connected stages: preprocessing of receptor and ligand inputs, docking execution, postproc
