A large descriptor set and a probabilistic kernel-based classifier   significantly improve druglikeness classification

doi:10.1021/ci700107y

CORC > 北京大学 > 化学与分子工程学院

	A large descriptor set and a probabilistic kernel-based classifier significantly improve druglikeness classification
	Li, Qingliang ; Bender, Andreas ; Pei, Jianfeng ; Lai, Luhua
刊名	journal of chemical information and modeling
	2007
关键词	SUPPORT VECTOR MACHINES DRUG-LIKE PROPERTIES MOLECULAR DESCRIPTORS SELECTION CRITERIA PREDICTION LIKENESS NONDRUGS CHEMOINFORMATICS PERMEABILITY CHEMOMETRICS
DOI	10.1021/ci700107y
英文摘要	Probabilistic support vector machine (SVM) in combination with ECFP_4 (Extended Connectivity Fingerprints) were applied to establish a druglikeness filter for molecules. Here, the World Drug Index (WDI) and the Available Chemical Directory (ACD) were used as surrogates for druglike and nondruglike molecules, respectively. Compared with published methods using the same data sets, the classifier significantly improved the prediction accuracy, especially when using a larger data set of 341 601 compounds, which further pushed the correct classification rates up to 92.73%. On the other hand, most characteristic features for drugs and nondrugs found by the current method were visualized, which might be useful as guiding fragments for de novo drug design and fragment based drug design.; Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; SCI(E); EI; 0; ARTICLE; 5; 1776-1786; 47
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/249777]
专题	化学与分子工程学院
推荐引用方式 GB/T 7714	Li, Qingliang,Bender, Andreas,Pei, Jianfeng,et al. A large descriptor set and a probabilistic kernel-based classifier significantly improve druglikeness classification[J]. journal of chemical information and modeling,2007.
APA	Li, Qingliang,Bender, Andreas,Pei, Jianfeng,&Lai, Luhua.(2007).A large descriptor set and a probabilistic kernel-based classifier significantly improve druglikeness classification.journal of chemical information and modeling.
MLA	Li, Qingliang,et al."A large descriptor set and a probabilistic kernel-based classifier significantly improve druglikeness classification".journal of chemical information and modeling (2007).