SMARTDock: A Toolkit for the Automated Development of Target-Specific Scoring Functions Using Bioactivity Data

Molecular docking has become an essential tool in the early stages of structure-based drug discovery, enabling rapid virtual screening of large compound libraries against biological targets. However, the accuracy of binder selection is often limited by the available scoring functions. Here, we present a novel workflow SMARTDock (Scoring with Machine learning and Activity for Ranking Targeted Docking) that enhances the virtual screening capabilities of GOLD docking by integrating publicly available bioactivity data, a protein-ligand interaction fingerprint (PADIF), and machine learning classification models within a user-friendly Docker environment. This platform-independent approach enables seamless use on different operating systems and is accessible to both computational and medicinal chemists. With only a ChEMBL target ID, a protein structure file, and a SMILES list of testing compounds, users can build and apply target-specific scoring models to improve the enrichment of active compounds in the top ranks. SMARTDock implements the PADIF-based ML methodology to assist in virtual screening. Previous validation of this underlying methodology demonstrated its capacity to enhance screening performance across multiple targets. Finally, we show the advantages and disadvantages in the bioactive classification in virtual screening tasks.