Ben-Gurion University of the Negev, Department of Software and Information Systems Engineering, Beer-Sheva, 8410501, Israel.
Universidade Federal Fluminense, Instituto de Biologia, Niterói, 24220900, Brazil.
Sci Data. 2022 Jun 2;9(1):263. doi: 10.1038/s41597-022-01360-z.
In recent years, due to the complementary action of drug combinations over mono-therapy, the multiple-drugs for multiple-targets paradigm has received increased attention to treat bacterial infections and complex diseases. Although new drug combinations screening has benefited from experimental tests like automated high throughput screening, it is limited due to the large number of possible drug combinations. The task of drug combination screening can be streamlined through computational methods and models. Such models require up-to-date databases; however, existing databases are static and consist of the data collected at the time of their creation. This paper introduces the Continuous Drug Combination Database (CDCDB), a continuously updated drug combination database. The CDCDB includes over 40,795 drug combinations, of which 17,107 are unique combinations consisting of more than 4,129 individual drugs, curated from ClinicalTrials.gov, the FDA Orange Book, and patents. To create CDCDB, we use various methods, including natural language processing techniques, to improve the process of drug combination discovery, ensuring that our database can be used for drug synergy prediction. Website: https://icc.ise.bgu.ac.il/medical_ai/CDCDB/ .
近年来，由于药物组合的互补作用优于单药治疗，因此针对细菌感染和复杂疾病的多药物多靶点模式受到了越来越多的关注。虽然新的药物组合筛选得益于自动化高通量筛选等实验测试，但由于可能的药物组合数量众多，这种方法仍然受到限制。药物组合筛选的任务可以通过计算方法和模型来简化。这些模型需要最新的数据库；然而，现有的数据库是静态的，仅包含创建时收集的数据。本文介绍了连续药物组合数据库 (CDCDB)，这是一个不断更新的药物组合数据库。CDCDB 包含超过 40795 种药物组合，其中 17107 种是独特的组合，由超过 4129 种单一药物组成，这些药物组合是从 ClinicalTrials.gov、FDA 橙皮书和专利中整理出来的。为了创建 CDCDB，我们使用了各种方法，包括自然语言处理技术，以改进药物组合发现的过程，确保我们的数据库可用于药物协同作用预测。网址：https://icc.ise.bgu.ac.il/medical_ai/CDCDB/ 。
