README.md

metadata

license: cc-by-nc-4.0
license_link: https://creativecommons.org/licenses/by-nc/4.0/
pretty_name: batman
size_categories:
  - 100K<n<1M
configs:
  - config_name: batman_ingredients
    data_files: data/batman_ingredients.csv
    sep: "\t"
  - config_name: batman_herbs
    data_files: data/batman_herbs.csv
    sep: "\t"
  - config_name: batman_formulas
    data_files: data/batman_formulas.csv
    sep: "\t"
task_categories:
  - other
language:
  - en
  - cn
tags:
  - biology
  - drug discovery
  - chemical screening
  - life sciences
  - chemistry
  - medical

What is the purpose of this dataset?

This dataset is a reformatted replica of BATMAN-2.0, a database of traditional Chinese medicine (TCM) ingredients, herbs, and formulas. Refer to the original peer-reviewed publication for more detail.

What can I do with this dataset?

Check out the demo notebooks in the demo folder. BATMAN is a powerful source of data that links natural compounds and their compositions to protein targets. You can use this information to run drug discovery experiments or to extend the functionality of your own biomedical language models.

For example, we used this dataset to extend the capabilities of Precious3GPT in the demo/TCM_geroprotectors demo project. While P3GPT is a transformer that can generate omics signatures for various tissues and conditions, it lacks any information on herbal medicine and TCM formulations. With BATMAN, we managed to remove this drawback and enable TCM screening in P3GPT.

Dataset structure

• batman_ingredients: compounds naturally occurring in TCM herbs
  • UID: unique ID of an entity throughout the complete dataset;
  • cid: PubChem molecule ID;
  • pref_name: conventional name of a compound (as mentioned in BATMAN);
  • synonyms: other names associated with the CID (if present in BATMAN);
  • targets_known: Verified human protein targets, according to BATMAN. The format is "symbol(ENTREZ_ID)"
  • targets_pred: All predicted protein targets, as stated in BATMAN (no significance threshold was used to filter these lists);
  • herbs: A list of herbs, in which the compound is encountered;
  • formulas: A list of TCM formulas that feature this compound.
• batman_herb: herbs used in TCM
  • UID;
  • pref_name: Most commonly, the pinyin name of a herb. If not available — any other available name, e.g. latin or common English;
  • synonyms
  • ingredients: A list of all compound CIDs occurring in a herb;
  • formulas: A list of TCM formulas that feature thisherb.
• batman_formulas: TCM formulas featured in the BATMAN dataset;
  • UID;
  • pref_name: The pinyin name of a herbal drug. Non-identical compositions sharing the same name are disambiguated using the ~X suffix;
  • synonyms: The Chinese hieroglyphic spelling of the compostion;
  • ingredients;
  • herbs.

Cite this dataset

If you use this dataset in a published project make sure to cite the original academic article:

Kong X, Liu C, Zhang Z, Cheng M, Mei Z, Li X, Liu P, Diao L, Ma Y, Jiang P, Kong X, Nie S, Guo Y, Wang Z, Zhang X, Wang Y, Tang L, Guo S, Liu Z, Li D. BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins. Nucleic Acids Res. 2024 Jan 5;52(D1):D1110-D1120. (https://doi.org/10.1093/nar/gkad926)

@article{10.1093/nar/gkad926,
    author = {Kong, Xiangren and Liu, Chao and Zhang, Zuzhen and Cheng, Meiqi and Mei, Zhijun and Li, Xiangdong and Liu, Peng and Diao, Lihong and Ma, Yajie and Jiang, Peng and Kong, Xiangya and Nie, Shiyan and Guo, Yingzi and Wang, Ze and Zhang, Xinlei and Wang, Yan and Tang, Liujun and Guo, Shuzhen and Liu, Zhongyang and Li, Dong},
    title = "{BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins}",
    journal = {Nucleic Acids Research},
    volume = {52},
    number = {D1},
    pages = {D1110-D1120},
    year = {2023},
    month = {10},
    abstract = "{Traditional Chinese medicine (TCM) is increasingly recognized and utilized worldwide. However, the complex ingredients of TCM and their interactions with the human body make elucidating molecular mechanisms challenging, which greatly hinders the modernization of TCM. In 2016, we developed BATMAN-TCM 1.0, which is an integrated database of TCM ingredient–target protein interaction (TTI) for pharmacology research. Here, to address the growing need for a higher coverage TTI dataset, and using omics data to screen active TCM ingredients or herbs for complex disease treatment, we updated BATMAN-TCM to version 2.0 (http://bionet.ncpsb.org.cn/batman-tcm/). Using the same protocol as version 1.0, we collected 17 068 known TTIs by manual curation (with a 62.3-fold increase), and predicted ∼2.3 million high-confidence TTIs. In addition, we incorporated three new features into the updated version: (i) it enables simultaneous exploration of the target of TCM ingredient for pharmacology research and TCM ingredients binding to target proteins for drug discovery; (ii) it has significantly expanded TTI coverage; and (iii) the website was redesigned for better user experience and higher speed. We believe that BATMAN-TCM 2.0, as a discovery repository, will contribute to the study of TCM molecular mechanisms and the development of new drugs for complex diseases.}",
    issn = {0305-1048},
    doi = {10.1093/nar/gkad926},
    url = {https://doi.org/10.1093/nar/gkad926},
    eprint = {https://academic.oup.com/nar/article-pdf/52/D1/D1110/55040286/gkad926.pdf},
}