# Pkad R Eval

> This evaluation probes the ability of hybrid quantum-classical models to accurately predict residue-level pKa values across diverse protein microenvironments. It tests whether entanglement-aware quantum feature mappings generalize beyond the training distribution to experimental datasets and capture subtle electronic and geometric correlations in flexible peptide regions. Use when the user wants to benchmark on PKAD-R, Aβ40, or asks about evaluating this task. Reports RMSE.

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

---


# pkad-r-eval

> Hybrid Quantum-Classical Encoding for Accurate Residue-Level pKa Prediction — Van Le et al. (2026) (arXiv:2603.11061, 2026)

## What this evaluates

This evaluation probes the ability of hybrid quantum-classical models to accurately predict residue-level pKa values across diverse protein microenvironments. It tests whether entanglement-aware quantum feature mappings generalize beyond the training distribution to experimental datasets and capture subtle electronic and geometric correlations in flexible peptide regions.

## Datasets

- **PKAD-R** — total ?; splits: train (-1), test (-1)
- **Aβ40** — total 3; splits: test (3)

## Metrics

- `RMSE` **(primary)** — range: other
  - Root Mean Square Error: sqrt(mean((y_true - y_pred)^2)). Lower values indicate higher predictive accuracy.
- `MAE` — range: other
  - Mean Absolute Error: mean(|y_true - y_pred|). Measures average magnitude of prediction errors in pKa units.
- `MaxErr` — range: other
  - Maximum Absolute Error: max(|y_true - y_pred|). Captures the worst-case prediction deviation.
- `Pearson correlation (R)` — range: other
  - Pearson correlation coefficient between true and predicted pKa values. Higher values indicate stronger linear agreement with experiment.
- `Regression slope (m)` — range: other
  - Slope of the linear regression line fitting predicted values to true values. Values closer to 1 reflect stronger linear agreement.

## Input / output format

**Input**: Residue-level protein structures processed through an entanglement-aware quantum feature mapping to produce hybrid quantum-classical embeddings.

**Output**: Continuous pKa value prediction (float) per residue.

## Scoring recipe

```python
def compute_metrics(y_true, y_pred):
    import numpy as np
    from scipy.stats import pearsonr
    errors = y_true - y_pred
    rmse = np.sqrt(np.mean(errors**2))
    mae = np.mean(np.abs(errors))
    max_err = np.max(np.abs(errors))
    r, _ = pearsonr(y_true, y_pred)
    slope = np.polyfit(y_pred, y_true, 1)[0]
    return {'RMSE': rmse, 'MAE': mae, 'MaxErr': max_err, 'R': r, 'Slope': slope}
```

## Common pitfalls

- Overfitting to high-dimensional quantum feature space: models like GradientBoosting achieve near-zero training error but suffer severe degradation on test data due to fitting noise rather than transferable structure-function relationships.
- Sensitivity to structural flexibility and underrepresented regions: residues in highly dynamic or solvent-exposed environments yield higher prediction errors and variance due to limited training distribution coverage and higher experimental uncertainty.
- Misinterpreting correlation-based quantum features as capturing all local biochemical effects: the encoding emphasizes nonlocal electronic/geometric correlations, potentially missing local descriptors like solvent accessibility or backbone dihedrals.

## Evidence (verbatim from paper)

> Table I reports the performance of four representative models—DQNN, GradientBoosting, GPR_SE, and kNN—across RMSE, MAE, maximum absolute error, Pearson correlation (R), and regression slope (m). Lower RMSE, MAE, and MaxErr values indicate higher predictive accuracy, while higher R and slopes closer to 1 reflect stronger linear agreement with experimental measurements.

## Citation

```bibtex
@misc{vanle2026hybrid,
  title={Hybrid Quantum-Classical Encoding for Accurate Residue-Level pKa Prediction},
  author={Van Le et al. (2026)},
  year={2026},
  note={arXiv:2603.11061}
}
```

- arXiv: 2603.11061