jjingliu/approved_drug_target
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--- license: cc-by-nc-4.0 task_categories: - text-generation language: - en tags: - chemistry - biology - medical pretty_name: approved_drug_target size_categories: - 10K<n<100K configs: - config_name: approved_drug_target data_files: - split: train path: approved_drug_target.json - config_name: uniprot_sequence data_files: - split: uniprot_seq path: uniprotId_sequence_2024_11_01.json --- # Approved Drug SMILES and Protein Sequence Dataset This dataset provides a curated collection of approved drug Simplified Molecular Input Line Entry System (SMILES) strings and their associated protein sequences. Each small molecule has been approved by at least one regulatory body, ensuring the safety and relevance of the data for computational applications. The dataset includes 1,660 approved small molecules and their 2,093 related protein targets. # Dataset The data comes from the following sources: - DrugBank - UniProt - ChEMBL - ZINC20 # Data verification and processing A total of 1,710 approved small molecules were retrieved from the DrugBank database, 117 of which were labeled as withdrawn. After assessment by a physician (Ali Motahharynia) and a pharmacist (Mahsa Sheikholeslami), 50 withdrawn drugs were excluded due to safety concerns, resulting in 1,660 approved small molecules. 2,116 protein targets were associated with these drugs, but 27 proteins were missing or unverified in UniProt database. These were manually replaced or verified using UniProt IDs, identical protein names, or Basic Local Alignment Search Tool (BLAST) for alignment matching, ultimately leading to 2,093 verified protein targets. Protein with UniProt ID “Q5JXX5” was deleted from the UniProt database and was therefore excluded from the dataset. # Data structure - SMILES: Contains the SMILES strings for each of the approved molecules. These SMILES were retrieved from DrugBank, ChEMBL, and ZINC20 databases. - Sequences: Contains protein sequences retrieved from UniProt database. # You can load this dataset with: ```python from datasets import load_dataset dataset = load_dataset("alimotahharynia/approved_drug_target", "approved_drug_target") ``` You can also download the dataset directly in JSON format. # Citation If you use this dataset in your research, please cite our paper: ``` Sheikholeslami, M., Mazrouei, N., Gheisari, Y., Fasihi, A., Irajpour, M., & Motahharynia, A*. DrugGen enhances drug discovery with large language models and reinforcement learning. Sci Rep 15, 13445 (2025). https://doi.org/10.1038/s41598-025-98629-1 ```
license: CC BY-NC 4.0（知识共享署名-非商业性使用4.0国际许可协议） task_categories: - 文本生成 language: - 英语 tags: - 化学 - 生物学 - 医学 pretty_name: 已获批药物靶点（approved_drug_target） size_categories: - 10K<n<100K configs: - config_name: approved_drug_target data_files: - split: 训练集（train） path: approved_drug_target.json - config_name: uniprot_sequence data_files: - split: uniprot_seq path: uniprotId_sequence_2024_11_01.json --- # 已获批药物SMILES与蛋白质序列数据集 本数据集经精心整理，收录了已获批药物的简化分子线性输入规范（Simplified Molecular Input Line Entry System, SMILES）字符串及其关联的蛋白质序列。每一种小分子药物均至少经一个国家监管机构批准，确保了数据集在计算应用中的安全性与适用性。本数据集包含1660种已获批小分子药物，以及与之对应的2093个相关蛋白质靶点。 ## 数据集来源 本数据集的数据来源于以下数据库： - DrugBank - UniProt - ChEMBL - ZINC20 ## 数据验证与处理 研究团队从DrugBank数据库中共检索到1710种已获批小分子药物，其中117种被标记为已撤市。经内科医师Ali Motahharynia与药剂师Mahsa Sheikholeslami评估后，出于安全性考量排除了50种撤市药物，最终得到1660种符合要求的已获批小分子药物。这些药物共关联2116个蛋白质靶点，但其中27个蛋白质在UniProt数据库中存在缺失或未经验证的情况。研究人员通过UniProt标识符、一致蛋白质名称或局部比对搜索工具（Basic Local Alignment Search Tool, BLAST）进行序列比对匹配，手动完成了靶点的替换或验证工作，最终得到2093个经过严格验证的蛋白质靶点。UniProt标识符为Q5JXX5的蛋白质已从UniProt数据库中删除，因此被排除出本数据集。 ## 数据结构 - SMILES字段：存储每种已获批小分子药物的SMILES字符串，相关数据来源于DrugBank、ChEMBL与ZINC20数据库。 - 序列（Sequences）字段：存储从UniProt数据库中检索得到的蛋白质序列。 ## 数据集加载方式 可通过以下Python代码加载本数据集： python from datasets import load_dataset dataset = load_dataset("alimotahharynia/approved_drug_target", "approved_drug_target") 您也可以直接以JSON格式下载本数据集。 ## 引用方式 若您在研究工作中使用本数据集，请引用以下论文： Sheikholeslami, M., Mazrouei, N., Gheisari, Y., Fasihi, A., Irajpour, M., & Motahharynia, A*. DrugGen基于大语言模型（Large Language Model, LLM）与强化学习助力药物发现. 《科学报告》(Sci Rep) 15卷, 13445 (2025). https://doi.org/10.1038/s41598-025-98629-1
