SuperPred: update on drug classification and target prediction.
SuperPred: update on drug classification and target prediction.
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DOI：
10.1093/nar/gku477
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发表时间：
2014-07
影响因子：
14.9
通讯作者：
Preissner R
中科院分区：
文献类型：
--
作者：
Nickel J;Gohlke BO;Erehman J;Banerjee P;Rong WW;Goede A;Dunkel M;Preissner R
SuperPred Web服务器将类药物化合物的化学相似性与分子靶点和基于相似性质原理的治疗方法联系起来。自该服务器第一次发布以来，已知的复合目标相互作用的数量已经从7000个增加到66.5万个，这不仅允许更好的预测质量，而且还可以估计置信度。除了增加定量结合数据和统计考虑所有药物类别中的相似性分布外，还采用了新的方法来改进靶点预测。还考虑了3D相似性以及碎片的出现和物理化学性质的一致性。此外，还考察了不同指纹图谱对预测结果的影响。药物类别的回溯性预测(世界卫生组织的ATC代码)允许对一组特征良好的已批准药物的方法和描述符进行评估。预测精度提高了7.5%，总准确率达到75.1%。对于具有足够结构相似性的查询化合物，网络服务器允许对新化合物的医学适应区进行预测，并为已知目标找到新的线索。SuperPred无需注册即可在以下网址公开提供：http://prediction.charite.de.
The SuperPred web server connects chemical similarity of drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound–target interactions has increased from 7000 to 665 000, which allows not only a better prediction quality but also the estimation of a confidence. Apart from the addition of quantitative binding data and the statistical consideration of the similarity distribution in all drug classes, new approaches were implemented to improve the target prediction. The 3D similarity as well as the occurrence of fragments and the concordance of physico-chemical properties is also taken into account. In addition, the effect of different fingerprints on the prediction was examined. The retrospective prediction of a drug class (ATC code of the WHO) allows the evaluation of methods and descriptors for a well-characterized set of approved drugs. The prediction is improved by 7.5% to a total accuracy of 75.1%. For query compounds with sufficient structural similarity, the web server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. SuperPred is publicly available without registration at: http://prediction.charite.de.
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影响因子：
14.9
作者：
NCBI Resource Coordinators
通讯作者：
NCBI Resource Coordinators
DOI：
10.1021/c160017a018
发表时间：
1965-01-01
期刊：
JOURNAL OF CHEMICAL DOCUMENTATION
影响因子：
--
作者：
MORGAN, HL
通讯作者：
MORGAN, HL
网络预测药物的解剖治疗化学代码
影响因子：
5.8
作者：
Wang, Yong-Cui;Chen, Shi-Long;Wang, Yong
通讯作者：
Wang, Yong
影响因子：
2.9
作者：
Duan, Jianxin;Dixon, Steven L.;Sherman, Woody
通讯作者：
Sherman, Woody
DOI：
10.1073/pnas.1320001111
发表时间：
2014-03-18
影响因子：
11.1
作者：
Reker, Daniel;Rodrigues, Tiago;Schneider, Gisbert
通讯作者：
Schneider, Gisbert