Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach.

Journal: Journal of chemical information and modeling
Published Date:

Abstract

The present contribution introduces a novel computational protocol called PyRMD2Dock, which combines the Ligand-Based Virtual Screening (LBVS) tool PyRMD with the popular docking software AutoDock-GPU (AD4-GPU) to enhance the throughput of virtual screening campaigns for drug discovery. By implementing PyRMD2Dock, we demonstrate that it is possible to rapidly screen massive chemical databases and identify those with the highest predicted binding affinity to a target protein. Our benchmarking and screening experiments illustrate the predictive power and speed of PyRMD2Dock and highlight its potential to accelerate the discovery of novel drug candidates. Overall, this study showcases the value of combining AI-powered LBVS tools with docking software to enable effective and high-throughput virtual screening of ultralarge molecular databases in drug discovery. PyRMD and the PyRMD2Dock protocol are freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.

Authors

  • Michele Roggia
    DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy.
  • Benito Natale
    DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy.
  • Giorgio Amendola
    DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy.
  • Salvatore Di Maro
    DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy.
  • Sandro Cosconati
    DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy.