Tassy24/K-Paths-inductive-reasoning-ddinter

🔗 This dataset is part of the study:

K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction

• 📖 Read the Paper
• 💾 GitHub Repository

DDInter: Inductive Reasoning Dataset

DDInter provides drug–drug interaction (DDI) data labeled with three severity levels (Major, Moderate, Minor). Each entry includes two drugs, an interaction label, drug descriptions, and structured/natural language representations of multi-hop reasoning paths between them. The multi-hop paths were extracted from a biomedical knowledge graph (Hetionet) using K-Paths.

This dataset is useful for evaluating:

• Path-based biomedical reasoning
• Knowledge graph inference
• Inductive reasoning

💡 Columns

Column	Description
drug1_db, drug2_db	DrugBank IDs
drug1_id, drug2_id	Node IDs from the KG
drug_pair	Optional drug pair identifier
drug1_name, drug2_name	Names of each drug
drug1_desc, drug2_desc	Descriptions of each drug
label	Interaction severity (e.g., Moderate)
label_idx	Numeric version of the label
all_paths, all_paths_str, path_str	KG paths for reasoning

📦 Example Record

{
  "drug1_name": "Mannitol",
  "drug2_name": "Castor oil",
  "label": "Moderate",
  "path_str": "Mannitol (Compound) binds ABCB1 (Gene) and ABCG2 (Gene)..."
}

## 📥 How to Load with pandas

```python
import pandas as pd

splits = {
    "train": "ddinter/train.json",
    "test": "ddinter/test.json"
}

train = pd.read_json("hf://datasets/Tassy24/K-Paths-inductive-reasoning-ddinter/" + splits["train"], lines=True)
test = pd.read_json("hf://datasets/Tassy24/K-Paths-inductive-reasoning-ddinter/" + splits["test"], lines=True)



### 📜 Citation (BibTeX)

```bibtex
@article{abdullahi2025k,
  title={K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction},
  author={Abdullahi, Tassallah and Gemou, Ioanna and Nayak, Nihal V and Murtaza, Ghulam and Bach, Stephen H and Eickhoff, Carsten and Singh, Ritambhara},
  journal={arXiv preprint arXiv:2502.13344},
  year={2025}
}