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Context <p>In 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 <a href="https://smashreleases.z13.web.core.windows.net/">https://smashreleases.z13.web.core.windows.net/</a></p> Methods <p>We 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.</p>

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SMASH: Screening Molecules Accurately on Small Hardware. Fast, user-friendly, enhanced with a machine learning virtual screening tool

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月4日

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  • Alfredo Suárez-Alonso,
  • Leonardo D. Herrera-Zúñiga,
  • Mayra Lozano-Espinosa,
  • Abraham Giacoman-Martínez,
  • Edgar F. Alarcón-Villaseñor,
  • Julio C. Almanza-Pérez,
  • Francisco J. Alarcón-Aguilar

摘要

Context

In 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/

Methods

We 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.