Work

On the rise of AI technologies for virtual screening

Year: 2025

Type: article

Abstract: Here we provide raw data to reproduce results presented in our recent manuscript "On the rise of AI technologies for virtual screening" submitted to JCIM. The data allow to: reproduce the ROC curves based on Boltz-2 predictions for all targets of the ULVSH dataset (ZIP) analyse the Boltz-2 classifications based on different sets of runtime parameters (XLS) explore the structural models predicted by Boltz-2 for all protein-ligand complexes of the ULVSH dataset (pymol sessions, PSE)

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17804166

Author: Cecchini, Marco

Institution: Université de Strasbourg

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: Allostery in Drug Discovery