Prediction of drug indications based on chemical interactions and chemical similarities.
Prediction of drug indications based on chemical interactions and chemical similarities.
复制标题
基于化学相互作用和化学相似性的药物适应症预测
DOI：
10.1155/2015/584546
复制
发表时间：
2015
影响因子：
--
通讯作者：
Cai YD
中科院分区：
文献类型：
--
作者：
Huang G;Lu Y;Lu C;Zheng M;Cai YD
发现新型或已批准药物的潜在适应症是药物开发的关键一步。以往的计算方法根据问题的出发点可以分为以疾病为中心和以药物为中心，根据数据集的多样性可以分为小规模应用和大规模应用。在这里，已经构建了一个分类器，以基于以下假设来预测药物的适应症：使用大的药物适应症数据集，相互作用/相关的药物或具有相似结构的药物更可能靶向相同的疾病。为了检查分类器，对从综合药物化学数据库检索的1，573种药物的数据集进行了5次，通过5倍交叉验证进行评估，产生了5个一阶预测准确度，均约为51.48%。同时，该模型产生了50.00%的准确率为一阶预测的数据集与其他32种药物，其中药物重新定位已被证实的独立测试。有趣的是，我们的方法成功地识别了一些未包含在数据集中的临床重新使用的药物适应症。这些结果表明，我们的方法可能成为一种有用的工具，将新的分子与新的适应症或现有药物的替代适应症联系起来。
Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.
登录
查看更多内容
DOI：
10.1021/ci010132r
发表时间：
2002-11-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Durant, JL;Leland, BA;Nourse, JG
通讯作者：
Nourse, JG
影响因子：
56.9
作者：
ELION, GB
通讯作者：
ELION, GB
影响因子：
5.8
作者：
Bhasin, M;Raghava, GPS
通讯作者：
Raghava, GPS
影响因子：
20.3
作者：
Fehniger, Todd A.;Byrd, John C.;Blum, William
通讯作者：
Blum, William
药物解剖治疗化学类别预测和重新定位的混合方法
影响因子：
--
作者：
Chen, Lei;Lu, Jing;Cai, Yu-Dong
通讯作者：
Cai, Yu-Dong