This README file was generated on 2025-12-04 by Marco Cecchini.
Last updated: 2025-12-04.
 
# GENERAL INFORMATION
 
## Dataset title: On the rise of AI technologies for virtual screening
 
## DOI: 10.5281/zenodo.17804167
 
## Contact email: mcecchini@unistra.fr
 
# METHODOLOGICAL INFORMATION 
 
## Environmental/experimental conditions: 
Benchmark of ten ligand classification problems by Boltz-2, a recently emerged AI-based technology for predicting protein-ligand interaction including binding affinities.
 
## Description of sources and methods used to collect and generate data:
Boltz-2 (https://github.com/jwohlwend/boltz)
ULVSH dataset (http://bioinfo-pharma.u-strasbg.fr/labwebsite/downloads/ULVSH.tgz)

## Methods for processing the data:
see companion paper (under review)
 
## Quality -assurance procedures performed on the data: 
see companion paper (under review)

# DATA & FILE OVERVIEW
 
## File naming convention:

boltz2-default.zip
Raw data to reproduce the ROC curves based on Boltz-2 predictions for all targets (TXT)

Boltz2_ULVSH-data.xlsx
Ligand classifications based on different sets of run-time Boltz-2 parameters (XLS)

all_aligned_XXX.pse
Structural models predicted by Boltz-2 for all protein-ligand complexes of the ULVSH dataset provided as pymol sessions (PSE)