ArticleJournal of molecular modeling2026
SMASH: Screening Molecules Accurately on Small Hardware. Fast, user-friendly, enhanced with a machine learning virtual screening tool.
Article in Journal of molecular modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
contextIn 2025, we released UAM-Ixachi to democratize, simplify, and accelerate molecular docking and virtual screening methods. It is a free, open-source, and user-friendly tool. Building on that work, we present SMASH, which features several upgrades: it predicts binding sites using machine learning, automatically determines titration states, utilizes a Graphics Process Unit for molecular docking, combines machine learning with energy scoring functions, and clusters data using the K-means algorithm. Tests demonstrated that the tool might handle large projects within a reasonable time using local computational resources. In automatic mode, the tool can accurately reproduce a high percentage of ligand poses from Protein Data Bank complexes via docking simulation. It also differentiated between the predictions of active and decoy ligands from a DUD-E data set in a reasonable amount of time. SMASH is freely available at https://smashreleases.z13.web.core.windows.net/
methodsWe implemented several software solutions to prepare and execute molecular docking simulations: PDB2PQR, P2Rank, MGL Tools, OpenBabel, AutoDock-GPU, Vina-GPU, and SCORCH. The MMFF94, AD4, and Vina force fields are part of the implemented tools.
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