Abstract：
: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 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.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Ma C,Wang L,Yang P,Myint KZ,Xie XQdoi
10.1021/ci3003914subject
Has Abstractpub_date
2013-01-28 00:00:00pages
11-26issue
1eissn
1549-9596issn
1549-960Xjournal_volume
53pub_type
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