Moleculenet Property Prediction Eval

---
name: moleculenet-property-prediction-eval
description: Evaluates the ability of multimodal molecular representation models to predict diverse physicochemical and biological properties from graph, text, and fingerprint inputs. It probes both classification (binary/multi-label activity prediction) and regression (continuous property estimation) capabilities across standardized chemical benchmarks. Use when the user wants to benchmark on MoleculeNet, or asks about evaluating this task. Reports ROC-AUC, RMSE.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2601.22610
  bibtex_key: liu2026localglobalmultimodal
  confidence: high
---

# moleculenet-property-prediction-eval

> Local-Global Multimodal Contrastive Learning for Molecular Property Prediction — Liu et al. (2026) (arXiv:2601.22610, 2026)

## What this evaluates

Evaluates the ability of multimodal molecular representation models to predict diverse physicochemical and biological properties from graph, text, and fingerprint inputs. It probes both classification (binary/multi-label activity prediction) and regression (continuous property estimation) capabilities across standardized chemical benchmarks.

## Datasets

- **MoleculeNet** — total ?; splits: train (-1), val (-1), test (-1); repo https://github.com/lhb0189/LGM-CL

## Metrics

- `ROC-AUC` **(primary)** — range: [0, 1]
  - Area under the Receiver Operating Characteristic curve, measuring the trade-off between true positive rate and false positive rate across classification thresholds.
- `RMSE` **(primary)** — range: other
  - Root Mean Squared Error, calculated as the square root of the average of squared differences between predicted and actual continuous values.

## Input / output format

**Input**: Molecular graph structures (nodes/edges), SMILES strings, LLM-enhanced textual descriptions, and molecular fingerprint features.

**Output**: Predicted class labels (for classification) or continuous property values (for regression).

## Scoring recipe

```python
def compute_metric(y_true, y_pred, task_type):
    if task_type == 'classification':
        return roc_auc_score(y_true, y_pred)
    else:
        return np.sqrt(mean_squared_error(y_true, y_pred))
```

## Common pitfalls

- The paper primarily reports results using random splitting (0.6/0.2/0.2); scaffold splitting results are only in the supplementary and should not be conflated with main results.
- Metrics are task-dependent: ROC-AUC is used for all 7 classification datasets, while RMSE is used for all 3 regression datasets. Mixing them up during comparison leads to incorrect conclusions.
- Performance must be averaged over three independent runs with different random seeds, as explicitly stated in the protocol.

## Evidence (verbatim from paper)

> Regarding data splitting, we follow the random splitting protocol adopted in S-CGIB *[lee2025pre]*, using a split ratio of $0.6/0.2/0.2$ for training, validation, and testing, and report the average performance over three runs with different random seeds. Performance is measured using ROC-AUC for classification tasks and RMSE for regression tasks.

## Citation

```bibtex
@misc{liu2026localglobalmultimodal,
  title={Local-Global Multimodal Contrastive Learning for Molecular Property Prediction},
  author={Liu et al. (2026)},
  year={2026},
  note={arXiv:2601.22610}
}
```

- arXiv: 2601.22610