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. 2024 Nov 18;25(22):12350. doi: 10.3390/ijms252212350

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© 2024 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Incorporating vScreenML 2.0 into a virtual screening pipeline. Candidate protein–ligand complexes can be generated by receptor-based methods (i.e., docking or pharmacophoric alignment to one or more known ligands). Scoring each of the resulting complexes with vScreenML 2.0 involves extracting “features” from each model using the PyRosetta (v. 2023.14+release.7132bdc), Binana (v.2.1), RF-Score (v1), RDKit (v.2023.9.5), LUNA (v.0.13.0), and PocketDruggability (v.0.98.1) packages. These features are used as input to vScreenML 2.0, which uses an XGBoost-based model to produce a single score reflecting the prioritization of the compounds in the screening collection.