屏蔽酚型抗氧剂的结构与抗氧化性能关系的计算机模拟

探讨了将计算机分子模拟技术应用于润滑油抗氧剂设计领域的方法和途径；利用计算机分子模拟技术考察了酚型抗氧剂的抗氧性能与分子的结构和性质之间的关系，建立了物理和化学意义和明确，预测能力较强的模型。表明，影响酚的抗氧性能的最主要因素是其最高被占分子轨道能力，分子构象能等性质。     

QSAR Study of Antimicrobial 3-Hydroxypyridine-4-one and 3-Hydroxypyran-4-one Derivatives Using Different Chemometric Tools

A series of 3-hydroxypyridine-4-one and 3-hydroxypyran-4-one derivatives were subjected to quantitative structure-antimicrobial activity relationships (QSAR) analysis. A collection of chemometrics methods, including factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR) and partial least squares combined with genetic algorithm for variable selection (GA-PLS) were employed to make connections between structural parameters and antimicrobial activity. The results revealed the significant role of topological parameters in the antimicrobial activity of the studied compounds against S. aureus and C. albicans. The most significant QSAR model, obtained by GA-PLS, could explain and predict 96% and 91% of variances in the pIC₅₀ data (compounds tested against S. aureus) and predict 91% and 87% of variances in the pIC₅₀ data (compounds tested against C. albicans), respectively.     

苯甲酸衍生物对兔子主动脉造粒系统的QSAR研究

应用分子力学方法MM 和半经验量子化学PM3法得到了32种苯甲酸衍生物的优势构象,利用量子化学算法和分子图形学技术获得电子结构、几何结构和拓扑结构参数,并将这些参数与苯甲酸衍生物在兔子主动脉造粒系统中的生理活性的大小相关联。结果表明:苯甲酸衍生物在兔子主动脉造粒系统中的生理活性大小与改进的分子连接性指数1J、第20号碳原子电荷Q和分子激化率P的关系密切。一下建立了32种苯甲酸衍生物的构效关系式。     

氨基喹啉类抗疟疾药物的活性预测

采用分子全息距离矢量方法描述40个氨基喹啉类化合物的分子结构,运用主成分回归方法建模进行定量构效关系分析,预测其抗疟原虫活性。其两组活性数据所得结果相关系数分别为0．9438和0．9737,交互检验相关系数分别为0．8305和0．9098。由此表明所建立的多参数模型稳定,能较好地预测氨基喹啉类药物的抗疟原虫活性。为指导和设计新的高效低毒抗疟疾药物提供有力依据。     

Modelling relationship between angiotensin-(I)-converting enzyme inhibition and the bitter taste of peptides

Relationships between angiotensin-(I)-converting enzyme inhibition and the bitter taste of peptides were studied. In cases where ACE inhibition or bitter taste had not been experimentally determined, their activity was estimated using several different peptide quantitative structure–activity relationship (QSAR) models. Significant correlations between increased ACE inhibition and bitterness were found for dipeptides using both observed and QSAR-predicted values. The relationship between ACE inhibition and bitter taste was attributed to the importance of hydrophobicity for both properties. Limited structural variations for dipeptides could make it difficult to have features that limit the effect of C-terminal hydrophobicity, necessary for ACE inhibition, on bitter taste. A similar modelling approach was also done on data from observed bitter oligopeptides derived from milk proteins. The relationship between QSAR-predicted ACE inhibition and observed bitter taste was not as strong as that found for dipeptides. Larger structural variation possibilities for oligopeptides than for dipeptides may thus make it, more feasible to find a highly efficient ACE inhibitory oligopeptide with a negligible bitter taste than a dipeptide.     

氟喹诺酮类药物与牛血清白蛋白结合的理论研究

在HF/6-31G水平对12种氟喹诺酮类药物分子进行量子化学计算,用ALOGPS 2.1程序计算了它们的正辛醇/水分配系数logP。相关性分析表明,氟喹诺酮与牛血清白蛋（BSA）主要靠静电作用相结合。通过多元线性回归建立了氟喹诺酮与BSA结合作用的定量构效关系（QSAR）模型。     

Investigations on inhibitors of hedgehog signal pathway: a quantitative structure-activity relationship study

The hedgehog signal pathway is an essential agent in developmental patterning, wherein the local concentration of the Hedgehog morphogens directs cellular differentiation and expansion. Furthermore, the Hedgehog pathway has been implicated in tumor/stromal interaction and cancer stem cell. Nowadays searching novel inhibitors for Hedgehog Signal Pathway is drawing much more attention by biological, chemical and pharmological scientists. In our study, a solid computational model is proposed which incorporates various statistical analysis methods to perform a Quantitative Structure-Activity Relationship (QSAR) study on the inhibitors of Hedgehog signaling. The whole QSAR data contain 93 cyclopamine derivatives as well as their activities against four different cell lines (NCI-H446, BxPC-3, SW1990 and NCI-H157). Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively. From these, demethylation is the best choice for inhibitor structure modifications. Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others.