LiCABEDS II. Modeling of ligand selectivity for G-protein-coupled cannabinoid receptors.
LiCABEDS II. Modeling of ligand selectivity for G-protein-coupled cannabinoid receptors.
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DOI：
10.1021/ci3003914
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发表时间：
2013-01-28
影响因子：
5.6
通讯作者：
Xie XQ
中科院分区：
文献类型：
--
作者：
Ma C;Wang L;Yang P;Myint KZ;Xie XQ
大麻素受体亚型2(CB2)是治疗血癌、缓解疼痛、骨质疏松症和免疫系统疾病的有效靶点。针对另一种密切相关的大麻素受体(CB1)的利莫那班最近的停用，强调了选择性对CB2配体开发的重要性，以将其对CB1受体的影响降至最低。在我们之前的研究中，LiCABEDS(自适应增强集成决策残基的配体分类器)被报道为一种用于预测分类分子性质的通用配体分类算法。在这里，我们报告了LiCABEDS的扩展应用到以分子指纹为描述符的大麻类配体选择性的建模中。从预测精度和召回率两个方面，系统地比较了LiCABEDS和另一种流行的分类算法--支持向量机的性能。此外，对LiCABEDS模型的检验表明，CB1和CB2选择性配体的结构多样性存在差异。来自数据挖掘的结构确定可用于新型大麻类先导化合物的设计。更重要的是，通过对新合成的CB2选择性化合物的成功鉴定，证明了LiCABEDS的潜力。
The cannabinoid receptor subtype 2 (CB2) is a promising therapeutic target for blood cancer, pain relief, osteoporosis, and immune system disease. The recent withdrawal of Rimonabant, which targets at another closely related cannabinoid receptor (CB1), accentuates the importance of selectivity for the development of CB2 ligands in order to minimize their effects on the CB1 receptor. In our previous study, LiCABEDS (Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps) was reported as a generic ligand classification algorithm for the prediction of categorical molecular properties. Here, we report extension of the application of LiCABEDS to the modeling of cannabinoid ligand selectivity with molecular fingerprints as descriptors. The performance of LiCABEDS was systematically compared with another popular classification algorithm, support vector machine (SVM), according to prediction precision and recall rate. In addition, the examination of LiCABEDS models revealed the difference in structure diversity of CB1 and CB2 selective ligands. The structure determination from data mining could be useful for the design of novel cannabinoid lead compounds. More importantly, the potential of LiCABEDS was demonstrated through successful identification of newly synthesized CB2 selective compounds.
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DOI：
10.1021/ci034207y
发表时间：
2004-01-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Bender, A;Mussa, HY;Reiling, S
通讯作者：
Reiling, S
影响因子：
4.8
作者：
Raduner, Stefan;Majewska, Adriana;Gertsch, Juerg
通讯作者：
Gertsch, Juerg
影响因子：
--
作者：
Cherkassky, V
通讯作者：
Cherkassky, V
影响因子：
9.7
作者：
Adams, CP;Brantner, VV
通讯作者：
Brantner, VV
影响因子：
5.6
作者：
Menon, Prashanthi;Yin, Guoyong;Smolock, Elaine M.;Zuscik, Michael J.;Yan, Chen;Berk, Bradford C.
通讯作者：
Berk, Bradford C.