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• W3180228137 abstract "Artificial intelligence (AI) algorithms are dramatically redefining the current drug discovery landscape by boosting the efficiency of its various steps. Still, their implementation often requires a certain level of expertise in AI paradigms and coding. This often prevents the use of these powerful methodologies by non-expert users involved in the design of new biologically active compounds. Here, the random matrix discriminant (RMD) algorithm, a high-performance AI method specifically tailored for the identification of new ligands, was implemented in a new fully automated tool, PyRMD. This ligand-based virtual screening tool can be trained using target bioactivity data directly downloaded from the ChEMBL repository without manual intervention. The software automatically splits the available training compounds into active and inactive sets and learns the distinctive chemical features responsible for the compounds' activity/inactivity. PyRMD was designed to easily screen millions of compounds in hours through an automated workflow and intuitive input files, allowing fine tuning of each parameter of the calculation. Additionally, PyRMD features a wealth of benchmark metrics, to accurately probe the model performance, which were used here to gauge its predictive potential and limitations. PyRMD is freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool." @default.
• W3180228137 created "2021-07-19" @default.
• W3180228137 creator A5040603165 @default.
• W3180228137 creator A5068576727 @default.
• W3180228137 date "2021-07-16" @default.
• W3180228137 modified "2023-10-16" @default.
• W3180228137 title "PyRMD: A New Fully Automated AI-Powered Ligand-Based Virtual Screening Tool" @default.
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• W3180228137 doi "https://doi.org/10.1021/acs.jcim.1c00653" @default.
• W3180228137 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34270903" @default.
• W3180228137 hasPublicationYear "2021" @default.
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