Dti Inductive Prediction Eval

---
name: dti-inductive-prediction-eval
description: Evaluates the ability of machine learning models to predict drug-target interactions in inductive settings where test drugs, targets, or both are unseen during training. It probes cold-start prediction capabilities and robustness to local class imbalance in sparse biological networks. Use when the user wants to benchmark on NR, GPCR, IC, E, DB, or asks about evaluating this task. Reports AUPR.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2012.12325
  bibtex_key: [REDACTED]
  confidence: high
---

# dti-inductive-prediction-eval

> Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery — Bin Liu et al. (2020) (arXiv:2012.12325, 2020)

## What this evaluates

Evaluates the ability of machine learning models to predict drug-target interactions in inductive settings where test drugs, targets, or both are unseen during training. It probes cold-start prediction capabilities and robustness to local class imbalance in sparse biological networks.

## Datasets

- **NR** — total 90; splits: S2 (drug-wise 10-fold CV) (-1), S3 (target-wise 10-fold CV) (-1), S4 (block-wise 3-fold CV) (-1)
- **GPCR** — total 635; splits: S2 (drug-wise 10-fold CV) (-1), S3 (target-wise 10-fold CV) (-1), S4 (block-wise 3-fold CV) (-1)
- **IC** — total 1476; splits: S2 (drug-wise 10-fold CV) (-1), S3 (target-wise 10-fold CV) (-1), S4 (block-wise 3-fold CV) (-1)
- **E** — total 2926; splits: S2 (drug-wise 10-fold CV) (-1), S3 (target-wise 10-fold CV) (-1), S4 (block-wise 3-fold CV) (-1)
- **DB** — total 3681; splits: S2 (drug-wise 10-fold CV) (-1), S3 (target-wise 10-fold CV) (-1), S4 (block-wise 3-fold CV) (-1)

## Metrics

- `AUPR` **(primary)** — range: [0, 1]
  - Area Under the Precision-Recall curve. Computed by integrating precision over recall thresholds across all predicted scores. Heavily penalizes highly ranked false positives.

## Input / output format

**Input**: Drug and target feature vectors or similarity matrices, along with the known drug-target interaction matrix (Y) for training. Test instances consist of unseen drug-target pairs requiring prediction.

**Output**: Continuous interaction scores or probabilities for each queried drug-target pair.

## Scoring recipe

```python
def compute_aupr(y_true, y_pred):
    from sklearn.metrics import precision_recall_curve, auc
    precision, recall, _ = precision_recall_curve(y_true, y_pred)
    return auc(recall, precision)
```

## Common pitfalls

- Failing to exclude test drug/target similarities from the training phase breaks the inductive assumption, leading to data leakage.
- Gold standard DTI datasets contain many missing interactions; evaluating on them without acknowledging missingness can underestimate true model performance.
- Local imbalance metrics (LI^d, LI^t) vary significantly across datasets and directly impact the difficulty of S2 vs S3 settings, requiring careful interpretation of cross-dataset results.

## Evidence (verbatim from paper)

> The Area Under the Precision-Recall curve (AUPR) which heavily punishes highly ranked false positive predictions [60] is used to evaluate the performance of inductive DTI prediction approaches in our experiments.

## Citation

```bibtex
@misc{liu2020drugtargetinteraction,
  title={Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery},
  author={Bin Liu et al. (2020)},
  year={2020},
  note={arXiv:2012.12325}
}
```

- arXiv: 2012.12325