DOI: 10.5281/zenodo.18721291
title: Property-Dependent Scoring Bias in Virtual Screening Validation: Data and Code

description: Complete dataset and analysis code for a dual scoring function virtual screening campaign against the human BCL-2 promoter G-quadruplex (G4) structure (PDB: 2F8U), with retrospective validation against the ROBIN (Repository Of BINders) experimental binding dataset.

This dataset accompanies the manuscript:Property-Dependent Scoring Bias in Virtual Screening Validation: Evidence from Dual Scoring Function Cross-Validation of BCL-2 G-Quadruplex Ensemble DockingTanigawa M. Journal of Computer-Aided Molecular Design (submitted).

Contents



Receptor structures: 10 NMR conformer ensemble (PDB: 2F8U) prepared for AutoDock Vina (PDBQT format)

AutoDock Vina docking data: Ensemble docking scores for 114 compounds across all 10 NMR models (1,140 calculations), including consensus rankings, per-model raw scores, and rank stability metrics

rDock cross-validation data: Ensemble docking scores for 113 compounds across all 10 NMR models (1,130 calculations), including consensus rankings and inter-model concordance

Cross-scoring comparison: Vina vs. rDock consensus rankings for 38 overlapping compounds, with molecular descriptor analysis of scoring function disagreement

Compound annotations: Comprehensive metadata for 114 compounds (33 fields) and Online Resource 1 with 251 compounds (35 fields), including SMILES, molecular properties, pharmacological classes, and dual scoring function rankings

Docking poses: PDBQT files for 114 compounds and 33 negative controls across 10 NMR models (AutoDock Vina)

Sigma-scaling perturbation analysis: Coordinate perturbation results for both scoring functions (20 compounds × 5 sigma levels × 3 replicates = 300 calculations per scoring function)

Discrimination analysis: G4-specific ligands vs. FDA-approved negative controls — Vina: AUROC = 0.748, p = 0.010 (n = 13 vs. 33); rDock: AUROC = 0.602, p = 0.162 (n = 11 vs. 32)

ROBIN retrospective validation: Ensemble docking of 111 experimentally confirmed BCL-2 G4 SMM binders and 700 negative controls (500 random + 200 property-matched) with both scoring functions, demonstrating AUROC ≈ 0.5 for both Vina and rDock, establishing that the curated library discrimination reflects property-dependent scoring bias rather than target-specific recognition

Supplementary figures: Online Resources 2–3 in PDF and PNG formats

Analysis pipeline: 49 Python scripts (35 curated library + 10 ROBIN validation + 4 figure generation) for full reproducibility


Key Findings




Metric
Vina
rDock




Compounds screened (single model)
7,469
7,296


Ensemble-docked compounds
114
113


Kendall's W (inter-model concordance)
0.651
0.454


AUROC (curated: G4-specific vs. negative controls)
0.748
0.602


AUROC (ROBIN: vs. random negatives)
0.466
0.493


AUROC (ROBIN: vs. property-matched negatives)
0.473
0.510


Cross-scoring Spearman ρ (38 compounds)
0.263 (p = 0.111)




Software



AutoDock Vina (52ec525-mod)

rDock 2013.1

fpocket 4.2

Open Babel 3.1.0

RDKit 2023.09+

Python 3.8+ with NumPy, Pandas, SciPy, BioPython, Matplotlib, Seaborn


Version History



v2.0: Added ROBIN retrospective validation data (robin_validation/ directory), rDock negative control ensemble docking, figure generation scripts, and supplementary figures

v1.0: Initial release with curated library docking data and dual scoring function cross-validation