Molecular Dynamics Eval

---
name: molecular-dynamics-eval
description: Evaluates a model's ability to reconstruct and predict time-varying 3D molecular surfaces in a continuous, resolution-independent manner. It measures volumetric overlap, point-to-point geometric distance, and surface normal alignment across diverse protein trajectories. Use when the user wants to benchmark on Sun et al. (2023) Protein Trajectories, or asks about evaluating this task. Reports volumetric IoU.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2601.22327
  bibtex_key: [REDACTED]
  confidence: high
---

# molecular-dynamics-eval

> Molecular Representations in Implicit Functional Space via Hyper-Networks — Wang et al. (2026) (arXiv:2601.22327, 2026)

## What this evaluates

Evaluates a model's ability to reconstruct and predict time-varying 3D molecular surfaces in a continuous, resolution-independent manner. It measures volumetric overlap, point-to-point geometric distance, and surface normal alignment across diverse protein trajectories.

## Datasets

- **Sun et al. (2023) Protein Trajectories** — total ?; splits: test (-1)

## Metrics

- `volumetric IoU` **(primary)** — range: [0, 1]
  - Intersection over Union of predicted and ground-truth volumetric masks. Higher is better.
- `Chamfer Distance (CD)` — range: other
  - Average squared Euclidean distance between points in the predicted surface and the ground-truth surface, and vice versa. Lower is better.
- `Normal Consistency (NC)` — range: [0, 1]
  - Mean cosine similarity between predicted and ground-truth surface normals. Higher is better.

## Input / output format

**Input**: 3D molecular trajectory data (time-varying atomic coordinates or surface points) representing protein conformations.

**Output**: Reconstructed continuous implicit neural representation or discrete surface mesh/predicted future frames.

## Scoring recipe

```python
iou = np.sum(pred_mask & gt_mask) / np.sum(pred_mask | gt_mask)
cd = 0.5 * (np.mean(np.min(pdist(pred_pts, gt_pts), axis=1)) + np.mean(np.min(pdist(gt_pts, pred_pts), axis=1)))
nc = np.mean(np.sum(pred_normals * gt_normals, axis=1))
```

## Common pitfalls

- IoU and CD are sensitive to grid resolution and sampling density, requiring consistent voxelization across methods.
- NC measures surface normal alignment, which can be unstable for highly fluctuating or low-signal trajectories.

## Evidence (verbatim from paper)

> We compare against NDF, DSR, and CanFields, using volumetric IoU, Chamfer Distance (CD), and Normal Consistency (NC) as evaluation metrics.

## Citation

```bibtex
@misc{wang2026molfield,
  title={Molecular Representations in Implicit Functional Space via Hyper-Networks},
  author={Wang et al. (2026)},
  year={2026},
  note={arXiv:2601.22327}
}
```

- arXiv: 2601.22327