Cdi Dti Dti Prediction Eval

cdi-dti-dti-prediction-eval

CDI-DTI: A Strong Cross-domain Interpretable Drug-Target Interaction Prediction Framework Based on Multi-Strategy Fusion — Xiangyu Li et al. (arXiv:2510.19520, 2025)

What this evaluates

Evaluates a multi-modal deep learning framework's ability to predict drug-target binding interactions across standard, cross-domain, and cold-start scenarios. It probes the model's capacity to integrate textual, structural, and functional biological features for robust binary classification under distribution shifts and unseen entities.

Datasets

• BindingDB — total ?; splits: train (-1), test (-1)
• Davis — total ?; splits: train (-1), test (-1)

Metrics

• Accuracy — range: [0, 1]
  • Ratio of correctly predicted binding interactions to the total number of predictions.
• F1-score — range: [0, 1]
  • Harmonic mean of precision and recall, balancing false positives and false negatives.
• AUROC (primary) — range: [0, 1]
  • Area under the receiver operating characteristic curve, measuring the model's ability to discriminate between binding and non-binding pairs across all classification thresholds.
• AUPRC — range: [0, 1]
  • Area under the precision-recall curve, emphasizing performance on the positive (binding) class, particularly useful for imbalanced datasets.

Input / output format

Input: Drug-target pair represented by multi-modal features: textual embeddings (ChemBERTa/ProtBERT), structural graphs (SMILES/AlphaFold), and functional embeddings (MoT5/DeepGO/BioBERT).

Output: Binary classification label (binding vs. non-binding) or interaction probability.

Scoring recipe

def compute_metrics(y_true, y_pred_proba, threshold=0.5):
    y_pred = (y_pred_proba >= threshold).astype(int)
    acc = np.mean(y_true == y_pred)
    f1 = f1_score(y_true, y_pred, average='binary')
    auroc = roc_auc_score(y_true, y_pred_proba)
    auprc = average_precision_score(y_true, y_pred_proba)
    return {'Accuracy': acc, 'F1-score': f1, 'AUROC': auroc, 'AUPRC': auprc}

Common pitfalls

• Dataset class imbalance requires careful handling of F1-score and AUPRC rather than relying solely on Accuracy.
• Cross-domain transfer (B→D, D→B) suffers from distribution shift, causing traditional single-modality models to degrade significantly.
• Cold-start scenarios (unDrug, unTarget, unPair) test generalization to completely unseen entities, which standard train/test splits do not capture.

Evidence (verbatim from paper)

We use accuracy, F1-score, AUROC (area under the receiver operating characteristic curve), and AUPRC (area under the precision-recall curve) as performance metrics.

Citation

@misc{li2025cdidti,
  title={CDI-DTI: A Strong Cross-domain Interpretable Drug-Target Interaction Prediction Framework Based on Multi-Strategy Fusion},
  author={Xiangyu Li et al.},
  year={2025},
  note={arXiv:2510.19520}
}

• arXiv: 2510.19520