What question did this study set out to answer?

The aim is to showcase the integration of AI technologies in virtual screening processes.

March 22, 20260 citationsOpen Access

On the rise of AI technologies in virtual screening

MCMarco CecchiniCentre National de la Recherche Scientifique

Key Points

The aim is to showcase the integration of AI technologies in virtual screening processes.
Shared raw data for ligand classification by Boltz-2 in the ULVSH dataset
Provided structural models for protein-ligand complexes
Collected docking hit lists from the LSD dataset for analysis
Structural models facilitate improved ligand classification
AI technologies contribute to effective virtual screening strategies

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)

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Marco Cecchini (Wed,) studied this question.

synapsesocial.com/papers/69bf3955c7b3c90b18b43ea8 https://doi.org/https://doi.org/10.5281/zenodo.19138090

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