Tassy24/K-Paths-inductive-reasoning-drugbank

🔗 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

DrugBank: Inductive Reasoning Dataset

This dataset contains drug pairs annotated with 86 pharmacological relationships (e.g.,DrugA may increase the anticholinergic activities of DrugB). 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": "Glycopyrronium",
  "drug2_name": "Cyclopentolate",
  "label": "Glycopyrronium may increase the anticholinergic effect...",
  "path_str": "Glycopyrronium (Compound) resembles Homatropine (Compound)..."
}

## 📥 How to Load with pandas

import pandas as pd

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

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