alimotahharynia/approved_disease_target_drug

Approved Drug SMILES, Protein Sequence, and Disease MeSH DAG Dataset

This dataset provides a curated collection of approved drug's simplified molecular input line entry system (SMILES) with their corresponding protein sequences, and associated disease's MeSH directed acyclic graph (DAG) structures. The dataset mainly consists of three parts, i.e., approved drugs with their related protein targets, target data with their related disease information, and approved drugs with their related disease information that are approved for that.

Dataset

The data comes from the following sources:

• —DrugBank
• —UniProt
• —ChEMBL
• —ZINC20
• —DisGeNET
• —UMLS

Data verification and processing

We created a curated dataset linking approved drugs with their corresponding targets and associated diseases. First, approved drug–target pairs were extracted from DrugBank database (version: 5.1.10). Next, disease-target associations were obtained from DisGeNET database (version: 3.12.1), which enabled the generation of preliminary disease-target-drug strings. To ensure clinical relevance, we incorporated approved drug-disease relationships (Phase IV) from the ChEMBL database (CHEMBL33). Preliminary strings were retained only if a direct drug-disease relationship was confirmed; otherwise, they were excluded. Following is the detailed information on dataset preparation.

• —Drug-target dataset We retrieved drug–target information from DrugBank, yielding 1,710 small molecules with annotated human targets. Of these,117 compounds were labeled as withdrawn. After a thorough assessment, 50 were excluded due to safety concerns or adverse effects. Available SMILES representations for the selected molecules (1,634 of 1,660) were obtained from DrugBank, ChEMBL, and ZINC20 database. From a total of 2116 related protein targets, 27 were not present in UniProt which were mapped to equivalent UniProt ID using reviewed UniProt ID, identical names, or the basic local alignment search tool (BLAST). The UniProt ID “Q5JXX5” was omitted from the database and therefore deleted from further analysis. Finally, the sequences for 2093 proteins were retrieved using the UniProt application programming interface (API).

• —Target-disease dataset We extracted target-disease information from DisGeNET. The disease concept unique identifier (CUI) was updated from version 2019AA to 2023AA, and corresponding MeSH terms were retrieved via the unified medical language system (UMLS) terminology API. In total, 608 MeSH terms were mapped to 2042 protein targets.

• —Drug-disease dataset We retrieved approved drug indications (disease-drug relationships) from ChEMBL. Following curation, 1,299 small molecules and their associated 643 MeSH terms were retained, forming the drug–disease dataset. To capture hierarchical disease relationships, MeSH terms were mapped to their corresponding DAG structures, resulting in 1,632 DAG representations.

Licensing and Data Attribution

This dataset is a curated, derived subset created for academic reproducibility by integrating approved drug–target, disease–target, and drug–disease associations obtained from DrugBank, DisGeNET, ChEMBL, UniProt, ZINC20, and UMLS. It does not reproduce the complete contents of any of these databases.

For comprehensive and up-to-date information, please refer to the official resources.

Use of DisGeNET data in commercial products or services requires a separate license from the DisGeNET/MedBioInformatics Solutions team.

Users are responsible for complying with the licensing terms, conditions of use, and citation requirements of the original data sources, including DrugBank, DisGeNET, ChEMBL, UniProt, ZINC20, and UMLS. Please refer to the official websites and license agreements of these resources for information regarding permitted use, redistribution, and commercial use, where applicable.

You can load this dataset with:

from datasets import load_dataset

dataset = load_dataset("alimotahharynia/approved_disease_target_drug", "approved_disease_target_drug")

You can also download the dataset directly in JSON format.

Citation

If you use this dataset in your research, please cite these papers:

@misc{motahharynia2026druggen2diseaseawarelanguage,
      title={DrugGen 2: A disease-aware language model for enhancing drug discovery}, 
      author={Ali Motahharynia and Mohammadreza Ghaffarzadeh-Esfahani and Mahsa Sheikholeslami and Navid Mazrouei and Matin Irajpour and Yousof Gheisari and Hajar Sirous},
      year={2026},
      eprint={2607.08404},
      archivePrefix={arXiv},
      primaryClass={q-bio.QM},
      url={https://arxiv.org/abs/2607.08404}, 
}

Piñero, Janet et al. “DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants.” Nucleic acids research vol. 45,D1 (2017): D833-D839. doi:10.1093/nar/gkw943