Wang Yi, Jin Yecheng, Zhou Chenguang, Qu Haibin, Cheng Yiyu
Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China.
Med Biol Eng Comput. 2008 Jun;46(6):605-11. doi: 10.1007/s11517-008-0323-1. Epub 2008 Mar 5.
Traditionally, active compounds were discovered from natural products by repeated isolation and bioassays, which can be highly time consuming. Here, we have developed a data mining approach using the casual discovery algorithm to identify active compounds from mixtures by investigating the correlation between their chemical composition and bioactivity in the mixtures. The efficacy of our algorithm was validated by the cytotoxic effect of Panax ginseng extracts on MCF-7 cells and compared with previous reports. It was demonstrated that our method could successfully pick out active compounds from a mixture in the absence of separation processes. It is expected that the presented algorithm can possibly accelerate the process of discovering new drugs.
传统上,活性化合物是通过反复分离和生物测定从天然产物中发现的,这可能非常耗时。在这里,我们开发了一种数据挖掘方法,使用因果发现算法,通过研究混合物中化学成分与生物活性之间的相关性,从混合物中识别活性化合物。我们算法的有效性通过人参提取物对MCF-7细胞的细胞毒性作用得到验证,并与先前的报告进行了比较。结果表明,我们的方法可以在不进行分离过程的情况下成功地从混合物中筛选出活性化合物。预计所提出的算法可能会加速新药发现的过程。