Moreza009/drug_approval_prediction

DrugReasoner Dataset

This dataset contains a curated collection of approved (Phase IV clinical trial) and unapproved (pre-clinical trial) small molecules from the ChEMBL database, annotated for drug approval status. It was designed for training and evaluating DrugReasoner, a reasoning-augmented LLM for drug approval prediction.

• Label: 1 for approved drugs, 0 for unapproved small molecules.
• Features: Each molecule is paired with its five most similar approved and unapproved molecules, based on a similarity metric.
• Source: All data were retrieved from ChEMBL database.
• Purpose: To support research in drug approval prediction, molecular reasoning, and interpretable AI in drug discovery.

Data verification and processing

A dataset of small molecules was obtained from the ChEMBL database (version 35) including approved molecules in Phase IV clinical trials and molecules in the preclinical phase. Child molecules were replaced by their parent compounds to avoid duplication. A subset of 2,255 approved and 2,255 unapproved molecules was created by random undersampling of the unapproved class. The simplified molecular input line entry system (SMILES) structure was retrieved, and strings were canonicalized using RDKit (version 2023.9.5). The dataset was partitioned into training, validation, and test subsets (8:1:1) using a stratified sampling strategy to maintain class distribution across all splits. An independent external dataset includes data presented in the ChemAP paper (Cho, C. et al., 2024). This dataset contained 20 approved and 8 unapproved drugs. Three approved drugs overlapping with the training, validation, or test sets were removed, and the remaining molecules (17 approved and 8 unapproved) were used for external evaluation.

Citation

If you use this dataset in your research, please cite our paper:

@misc{ghaffarzadehesfahani2025drugreasonerinterpretabledrugapproval,
      title={DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model}, 
      author={Mohammadreza Ghaffarzadeh-Esfahani and Ali Motahharynia and Nahid Yousefian and Navid Mazrouei and Jafar Ghaisari and Yousof Gheisari},
      year={2025},
      eprint={2508.18579},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.18579}, 
}

Datast Card Author

Mohammadreza Ghaffarzadeh-Esfahani

Dataset Card Contact

You can contact with the Corresponding author of the paper: Ali Motahharynia ([email protected])