# Dud E Virtual Screening Eval

> Ranks active compounds against decoys for a given protein target. It probes the model's ability to prioritize true binders in a large pool of inactive decoys and resist dataset biases. Use when the user wants to benchmark on DUD-E, AD, or asks about evaluating this task. Reports AUC.

- Skill: `qhjqhj00/dud-e-virtual-screening-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/dud-e-virtual-screening-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/dud-e-virtual-screening-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/dud-e-virtual-screening-eval

---


# dud-e-virtual-screening-eval

> Protein-ligand binding representation learning from fine-grained interactions — Feng et al. (2023) (arXiv:2311.16160, 2023)

## What this evaluates

Ranks active compounds against decoys for a given protein target. It probes the model's ability to prioritize true binders in a large pool of inactive decoys and resist dataset biases.

## Datasets

- **DUD-E** — total ?; splits: 3-fold CV (-1)
- **AD** — total ?; splits: test (-1)

## Metrics

- `AUC` **(primary)** — range: [0, 1]
  - Area Under the ROC Curve (AUROC). Higher values indicate better ranking of actives over decoys.
- `ROC Enrichment (RE)` — range: other
  - Number of active compounds in the top X% of the ranked list divided by (total actives * X%). Evaluated at 0.5%, 1.0%, 2.0%, 5.0%.

## Input / output format

**Input**: CLS token embeddings of pocket and ligand concatenated, passed through an MLP.

**Output**: Binary classification score (active vs inactive).

## Scoring recipe

```python
auc = compute_auroc(scores, labels)
for pct in [0.5, 1.0, 2.0, 5.0]:
  top_k = ceil(pct/100 * len(scores))
  re[pct] = sum(labels[top_k]) / (total_actives * pct/100)
```

## Common pitfalls

- DUD-E contains decoy bias (topological vs physical properties), which artificially inflates supervised methods' performance.
- Uses 3-fold cross-validation; similar targets are kept within the same fold to ensure fair comparison.

## Evidence (verbatim from paper)

> We employ a 3-fold cross-validation for training and evaluation, and our dataset split setting is consistent with AttentionDTI and DrugVQA, ensuring that similar targets are kept within the same fold to facilitate a fair comparison. Several widely used measures on DUD-E are employed in our evaluation, including AUROC and the ROC Enrichment metric (denoted as RE).

## Citation

```bibtex
@misc{feng2023proteinligand,
  title={Protein-ligand binding representation learning from fine-grained interactions},
  author={Feng et al. (2023)},
  year={2023},
  note={arXiv:2311.16160}
}
```

- arXiv: 2311.16160