On the rise of AI technologies in virtual screening

We provide the raw data to reproduce results presented in our manuscript "On the rise of AI technologies in virtual screening" submitted to JCIM. The data include: The raw data required to reproduce ligand classification by Boltz-2 in the ULVSH dataset (TXT) Structural models by Boltz-2 for all protein-ligand complexes in the ULVSH dataset (pymol sessions) The docking hit lists from the LSD dataset analyzed in this work. These lists were produced by extracting the top-1000 post-docking hits per target and mixing them with all experimentally confirmed actives including those that were ranked lower by docking (CSV)

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-03
DOI
https://doi.org/10.5281/zenodo.19138090
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

On the rise of AI technologies in virtual screening

Marco Cecchini
Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

On the rise of AI technologies in virtual screening

Marco Cecchini
article en

Abstract

We provide the raw data to reproduce results presented in our manuscript "On the rise of AI technologies in virtual screening" submitted to JCIM. The data include: The raw data required to reproduce ligand classification by Boltz-2 in the ULVSH dataset (TXT) Structural models by Boltz-2 for all protein-ligand complexes in the ULVSH dataset (pymol sessions) The docking hit lists from the LSD dataset analyzed in this work. These lists were produced by extracting the top-1000 post-docking hits per target and mixing them with all experimentally confirmed actives including those that were ranked lower by docking (CSV)

Zenodo (CERN European Organization for Nuclear Research)
Université de Strasbourg (FR)
Openalex Percentile: Top 21%
Computational Drug Discovery Methods
DOIOpen Access PDFOpenAlex
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