The approach involves a new 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, 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. PyRMD and the PyRMD2Dock protocol are freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.

PyRMD2Dock has the value of combining AI-powered LBVS tools with docking software to enable effective and high-throughput virtual screening of ultra-large molecular databases in drug discovery. It is leverages the advantages of SBVS harnessing the capabilities of AI-based VS.

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PyRMD, AutoDock-GPU

PyRMD: A New Fully Automated AI-Powered Ligand-Based Virtual Screening Tool by Giorgio Amendola and Sandro Cosconati. J. Chem. Inf. Model. 2021, 61, 8, 3835–3845. https://doi.org/10.1021/acs.jcim.1c00653

Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach by Michele Roggia, Benito Natale, Giorgio Amendola, Salvatore Di Maro and Sandro Cosconati submitted to J. Chem. Inf. Model.