几种改进的CoMFA方法比较研究血小板活化因子拮抗剂

由于传统的比较分子场分析(CoMFA)方法本身存在一些缺陷，使得分子的叠合 规则以及叠合分子的空间取向和空间位置等因素对q~2的影响很大，因此相继提出 了几种改进的CoMFA方法。为了优化CoMFA结果，应用传统的CoMFA方法和交叉验证 的R~2引导的区域选择法(q~2-GRS)、全取向搜索法（AOS）、全空间搜索法（APS） 以及比较分子相似性指数(CoMSIA)等四种改进的CoMFA方法，对18个pinusolide类 衍生物这类新发现的血小板活化因子（PAF）拮抗剂进行了比较研究。结果表明四 种改进的CoMFA方法得到的q~2值均比传统CoMFA的高。q~2-GRS方法得到的q~2值有 所提高，但综合结果并不理想，AOS与APS得到的q~2较为理想，而在CoMSIA中， q~2几乎不受空间取向或空间位置的影响。同时我们引人基于样本的偏最小二乘法 （SAMPLS）取代原AOS／APS程序中的传统PLS进行统计分析，明显提高了其运行速 度。最后，根据q~2最高的CoMFA模型和CoMSIA模型设计了几个预测活性更高的 pinusolide类似物。     

褪黑激素受体拮抗剂的三维定量构效关系研究

采用两种分子场分析方法即比较分子场分析法（CoMFA）和比较分子相似因子分析法（CoMSIA）进行了37个褪黑激素受体拮抗剂的构效关系研究.计算结果表明，两种方法得到的构效关系模型都具有较好的预测能力.在计算中，还考察了不同格点距离和电荷计算方法对构效关系模型的影响.通过分析分子场等值面图在空间的分布，可以观察到叠合分子周围分子场特征对化合物活性的影响，为设计新的褪黑激素拮抗剂提供了一些理论依据.     

3 D-QSAR Analysis of Agonists of nAChRs： Epibatidine Analogues

A 3 D-QSAR about nAChRs agonists epibatidine analogues was performed using theCoMFA and CoMSIA. The correlation coefficients were R2cv = 0.546, R2cv = 0.907 in CoMFA andR2cv = 0.655, R2,~ = 0.962 in CoMSIA of the final model. The prediction using the final models tothe test set was r2 = 0.675 in CoMFA and r2 = 0.462 in CoMSIA. This model will be useful in thedesign of novel compounds with high affinity.     

含呋喃环双酰脲类衍生物的三维定量构效关系研究

采用比较分子力场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA), 对27个新型双酰基脲类化合物的杀蚊幼虫(Aedes aegypti L.)活性进行三维定量构效关系(3D-QSAR)研究. 在CoMFA研究中, 考察了网格点步长对统计结果的影响. 在CoMSIA研究中, 系统考察了各种分子场组合、网格点步长和衰减因子对模型统计结果的影响, 发现立体场和氢键供体场的组合得到最佳模型. 所建立的CoMFA和CoMSIA模型的非交叉验证相关系数r2值分别为0.828和0.841, 并都具有较强的预测能力. CoMFA和CoMSIA模型的三维等值图不仅直观地解释了结构与活性的关系, 而且为后续优化该系列化合物提供了理论依据.     

新型三唑类抗真菌化合物的三维定量构效关系研究

采用比较分子力场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA), 系统研究了40个新型三唑类化合物抗真菌活性的三维定量构效关系. 在CoMFA研究中, 研究了两种药效构象对模型的影响, 并考察了网格点步长对统计结果的影响. 在CoMSIA研究中, 系统考察了各种分子场组合、网格点步长和衰减因子对模型统计结果的影响, 发现立体场、静电场、疏水场和氢键受体场的组合得到最佳模型. 所建立CoMFA和CoMSIA模型的交叉相关系数q²值分别为0.718和0.655, 并都具有较强的预测能力. CoMFA和CoMSIA模型的三维等值线图直观地解释了化合物的构效关系, 阐明了化合物结构中苯环上各位置取代基对抗真菌活性的影响, 为进一步结构优化提供了重要依据.     

Structural investigations of anthranilimide derivatives by CoMFA and CoMSIA 3D-QSAR studies reveal novel insight into their structures toward glycogen phosphorylase inhibition

In the present work, three-dimensional quantitative structure–activity relationship (3-D QSAR) studies on a set of 70 anthranilimide compounds has been performed using docking-based as well as substructure-based molecular alignments. This resulted in the selection of more statistically relevant substructure-based alignment for further studies. Further, molecular models with good predictive power were derived using CoMFA (r ^2?=?0.997; Q ^2?=?0.578) and CoMSIA (r ^2?=?0.976; Q ^2?=?0.506), for predicting the biological activity of new compounds. The so-developed contour plots identified several key features of the compounds explaining wide activity ranges. Based on the information derived from the CoMFA contour maps, novel leads were proposed which showed better predicted activity with respect to the already reported systems. Thus, the present study not only offers a highly significant predictive QSAR model for anthranilimide derivatives as glycogen phosphorylase (GP) inhibitors which can eventually assist and complement the rational drug-design attempts, but also proposes a highly predictive pharmacophore model as a guide for further development of selective and more potent GP inhibitors as anti-diabetic agents.     

Analysis of Ah receptor binding affinities of polybrominated diphenyl ethers via in silico molecular docking and 3D-QSAR

Polybrominated diphenyl ethers (PBDEs) have become ubiquitous contaminations due to their use as flame retardants. The structural similarity of PBDE to some dioxin-like compounds suggested that they may share similar toxicological effects: they might activate the aryl hydrocarbon receptor (AhR) signal transduction pathway and thus might have adverse effects on wildlife and humans. In this study, in silico computational workflow combining molecular docking and three-dimensional quantitative structure–activity relationship (3D-QSAR) was performed to investigate the binding interactions between PBDEs and AhR and the structural features affecting the AhR binding affinity of PBDE. The molecular docking showed that hydrogen-bond and hydrophobic interactions were the major driving forces for the binding of ligands to AhR, and several key amino acid residues were also identified. The CoMSIA model was developed from the conformations obtained from molecular docking and exhibited satisfactory results as q ² of 0.605 and r ² of 0.996. Furthermore, the derived model had good robustness and statistical significance in both internal and external validations. The 3D contour maps generated from CoMSIA provided important structural features influence the binding affinity. The obtained results were beneficial to better understand the toxicological mechanism of PBDEs.     

雌激素β受体喹啉类配体的分子对接及3D-QSAR研究

对33个喹啉衍生物的雌激素β受体活性进行了分子对接以及比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA). 对接结果显示氢键和疏水作用是配体与受体结合的主要因素,同时结果亦显示对接结合能与观测值pIC50具有极显著的线性相关性. 根据对接后各优势构象将33个样本进行叠合并进行CoMFA与CoMSIA研究,均得到了较优的结果,其中以选用立体场、静电场和疏水场建立的CoMSIA模型结果最优,其主成分数,r2,q2（LOO）和r2pred分别为2, 0.894, 0.708和0.802. 构效关系模型分析显示基团的空间位阻、电性及疏水作用是影响活性的主要因素