Molclr Molecular Property Eval

---
name: molclr-molecular-property-eval
description: Evaluates the ability of graph neural networks to learn robust molecular representations via self-supervised contrastive learning, and their transferability to downstream molecular property prediction tasks (classification and regression). Use when the user wants to benchmark on BBBP, Tox21, ClinTox, HIV, BACE, SIDER, MUV, FreeSolv, ESOL, Lipo, QM7, QM8, or asks about evaluating this task. Reports ROC-AUC.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2102.10056
  bibtex_key: wang2021molclr
  confidence: high
---

# molclr-molecular-property-eval

> Molecular Contrastive Learning of Representations via Graph Neural Networks — Wang et al. (2021) (arXiv:2102.10056, 2021)

## What this evaluates

Evaluates the ability of graph neural networks to learn robust molecular representations via self-supervised contrastive learning, and their transferability to downstream molecular property prediction tasks (classification and regression).

## Datasets

- **BBBP** — total 2039; splits: test (-1)
- **Tox21** — total 7831; splits: test (-1)
- **ClinTox** — total 1478; splits: test (-1)
- **HIV** — total 41127; splits: test (-1)
- **BACE** — total 1513; splits: test (-1)
- **SIDER** — total 1478; splits: test (-1)
- **MUV** — total 93087; splits: test (-1)
- **FreeSolv** — total 642; splits: test (-1)
- **ESOL** — total 1128; splits: test (-1)
- **Lipo** — total 4200; splits: test (-1)
- **QM7** — total 6830; splits: test (-1)
- **QM8** — total 21786; splits: test (-1)

## Metrics

- `ROC-AUC` **(primary)** — range: [0, 1]
  - Area under the Receiver Operating Characteristic curve. Measures the trade-off between true positive rate and false positive rate across classification thresholds.
- `MAE` — range: [0, ∞)
  - Mean Absolute Error. Computes the average of the absolute differences between predicted and true continuous values: MAE = (1/N) * Σ|y_true - y_pred|.

## Input / output format

**Input**: Molecule graph constructed from SMILES string, where nodes represent atoms and edges represent chemical bonds.

**Output**: Predicted molecular property value (continuous for regression, binary/multi-class label for classification).

## Scoring recipe

```python
def compute_roc_auc(y_true, y_pred_proba):
    return roc_auc_score(y_true, y_pred_proba)

def compute_mae(y_true, y_pred):
    return mean_absolute_error(y_true, y_pred)

# For multi-task datasets, compute metric per task and average across tasks.
```

## Common pitfalls

- Averaging ROC-AUC across tasks with different numbers of labels (e.g., Tox21 has 12 tasks, SIDER has 27) without specifying task-weighting vs dataset-weighting.
- Using non-standard train/validation/test splits, as these benchmarks have widely adopted fixed splits in the GNN literature.
- Confusing the pre-training contrastive loss objective with the downstream fine-tuning evaluation metric.

## Evidence (verbatim from paper)

> Table 1 demonstrates the test ROC-AUC performance of our MolCLR model on classification tasks in comparison to baseline models. The average and standard deviation of three individual runs are reported. Table 2 demonstrates the test MAE performance of our MolCLR model on regression benchmarks in comparison to baseline models.

## Citation

```bibtex
@misc{wang2021molclr,
  title={Molecular Contrastive Learning of Representations via Graph Neural Networks},
  author={Wang et al. (2021)},
  year={2021},
  note={arXiv:2102.10056}
}
```

- arXiv: 2102.10056