# Cns Drug Enrichment Eval

> Tests the model's ability to classify CNS-active versus CNS-inactive drugs and enrich active compounds from large virtual screening databases. It probes generalization on small-sample molecular datasets using external validation. Use when the user wants to benchmark on CNS Drug Dataset, or asks about evaluating this task. Reports AUC.

- Skill: `qhjqhj00/cns-drug-enrichment-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/cns-drug-enrichment-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/cns-drug-enrichment-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/cns-drug-enrichment-eval

---


# cns-drug-enrichment-eval

> Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction — Gao et al. (2023) (arXiv:2304.12239, 2023)

## What this evaluates

Tests the model's ability to classify CNS-active versus CNS-inactive drugs and enrich active compounds from large virtual screening databases. It probes generalization on small-sample molecular datasets using external validation.

## Datasets

- **CNS Drug Dataset** — total 1057; splits: train (940), test (117)

## Metrics

- `AUC` **(primary)** — range: [0, 1]
  - Area Under the ROC Curve for binary classification of CNS-active vs inactive drugs. Higher is better.

## Input / output format

**Input**: Molecular fingerprints-based bit vectors and binary labels (CNS-active or CNS-inactive).

**Output**: Binary classification probability or label.

## Scoring recipe

```python
def score_auc(y_pred, y_true):
    auc = roc_auc_score(y_true, y_pred)
    return auc
```

## Common pitfalls

- Relying solely on random splits without an external test set to assess generalization on unseen market drugs.
- Using traditional QSAR descriptors without leveraging 3D molecular representation learning for small-sample generalization.
- Skipping the initial fingerprint-based similarity search and rule-based filtering (Lipinski/Veber) before fine-grained screening.

## Evidence (verbatim from paper)

> We build CNS models using Uni-QSAR with 940 market drugs [72] (315 CNS-active and 625 CNS-inactive) as training data and evaluate models' performance using an additional external dataset of 117 market drugs [74] as testing data. The result in Fig. 5 shows that Uni-QSAR methods are more effective for the classification of CNS drugs versus non-CNS drugs. ... Validation AUC ... Testing AUC ...

## Citation

```bibtex
@misc{gao2023uniqsar,
  title={Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction},
  author={Gao et al. (2023)},
  year={2023},
  note={arXiv:2304.12239}
}
```

- arXiv: 2304.12239