Zhang Yanling

Support Vector Machine and KStar Models Predict the o-Dealkylation Reaction Mediated by Cytochrome P450

We constructed a nested prediction model based on support vector machines(SVM) and the KStar method.The models consisted of a molecular shape discriminative model for metabolites,which was used to predict the o-dealkalytion reaction mediated by cytochrome P450,in addition to the metabolic site discriminative model,which was used to judge C―O bond breaking in molecules.We calculated 1280 molecular descriptors including topological descriptors,2D autocorrelation descriptors,and geometric descriptors to characterize the physicochemical properties of 272 molecules.A molecular shape discriminative model,represented by the classification models,was constructed by machine learning methods including SVM,decision tree,Bayesian network,and k nearest neighbors method.The results showed that the SVM model was superior to the other methods.Twenty-six quantum chemical features including charge-related,valency-related,and energy-related features were calculated for the 538 metabolism sites for the o-dealkylation reaction in the metabolic site discriminative model.Machine learning methods including decision tree,Bayesian network,KStar,and the artificial neural network method were also used to develop classification models.It showed that the KStar model with its prediction accuracy,sensitivity,and specificity of more than 90% outperformed the other classification models.Fifteen traditional Chinese medicine medicinal molecules were used to validate the model.The results showed that the nested models had a certain accuracy and could contribute to the prediction of metabolites from traditional Chinese medicines.

Research on Material Base of TCM Property Based on Pharmacophore

Abstract Study on the material base of traditional Chinese medicine (TCM) property is critical for the modernization of TCM theory. This article proposed and proved a hypothesis that the common characters of the material base of Materia Medica (MM) determine TCM property. The identification technology of active clusters was used to identify the active clusters from the herbs of activating blood circulation (ABC), and the results were further used to research the intrinsic correlation between TCM property and the common characters of active compositions that were represented by pharmacophore. This research developed an innovative method for the study on the material base of TCM property.

穿心莲内酯通过下调PPARγ-C/EBPα抑制脂滴形成

目的探讨穿心莲内酯抑制脂肪细胞分化和脂滴堆积作用及机制。 方法将不同浓度穿心莲内酯分别作用于3T3-L1脂肪细胞，采用高内涵细胞成像技术，分析穿心莲内酯对脂滴形成的作用，并通过检测过氧化物酶体增殖物激活受体γ（PPARγ）和CCAAT/增强子结合蛋白α（C/EBPα）表达分析穿心莲内酯的分子作用机制。 结果穿心莲内酯剂量依赖降低脂滴堆积，在30 μmol/L可显著降低PPARγ和C/EBPα表达（P 0.05）。 结论穿心莲内酯通过降低PPARγ-C/EBPα表达抑制脂肪细胞分化，降低脂滴过度堆积，具有潜在的减轻肥胖作用。