SMILES2Docking: An Open-Source Desktop Workflow for Ligand Ionization, Stereochemistry-Aware Preparation and...

SMILES2Docking converts spreadsheet-based SMILES libraries into docking-ready 3D ligands through a single, configurable pipeline. Salt and coformer removal, a three-mode stereocenter policy, prediction of the dominant protonation state with a graph neural network pKa model (MolGpKa) refined by iterative titration, distance-geometry embedding with RDKit, a molecular-mechanics optimization cascade, and an optional semi-empirical refinement under an implicit solvation model are combined in one tool, with records processed either sequentially or distributed across CPU cores. Users select among permissive, strict, and enumerative stereocenter modes, so the same tool serves broad exploratory screening and precision docking. A per-compound JSON audit report records the ionization and stereocenter decisions taken for every structure. The tool is distributed as a Python package, a Windows executable with bundled MOPAC, a Linux portable bundle, and a macOS bundle, under GPL-2.0-or-later, and is freely available to non-commercial users at https://github.com/amgoncalvesusp/Smiles2Docking (DOI: 10.5281/zenodo.20617898).