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

📦 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

import pandas as pd

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

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

📜 Citation (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}
}