咪唑啉酮类除草剂的三维构效关系研究

从三维角度出发,采取不同的构象搜索方法得到了咪唑酮类化合物分子的活性构象。利用比较分子场分析方法进一步证实了模板分子构象的正确性。并从静电场、立体场及活性关系等方面进行了三维定量构效关系研究,得到了具有较强预测能力的QSAR模型。     

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.     

光系统Ⅱ(PSⅡ)抑制剂的比较分子场研究

以光系统Ⅱ抑制剂DISCO(DIStanceCOmparisons)模型的活性构象分子作为模板,利用比较分子场分析方法对三类结构不同的化合物进行了三维构效关系的研究。研究结果有助于对DISCO重叠模型的评估的新型PSⅡ抑制剂的设计与合成。     

Combretastatins类微管蛋白抑制剂的定量构效关系与结合模式

以Combretastatins的B环改造化合物为研究对象, 采用遗传函数分析方法进行了二维定量构效关系研究. 研究结果表明, Apol, PMI-mag, Dipole-mag, Hbond donor和RadOfGyration等描述符对该系列抑制剂活性的贡献最大. 采用比较分子场分析方法(CoMFA)和比较分子相似因子分析方法(CoMSIA)进行了三维定量构效关系研究, 建立的CoMFA和CoMSIA模型的交叉验证相关系数q2分别为0.630和0.634, 具有较强的预测能力. 利用CoMFA和CoMSIA模型的三维等势图解析了Combretastatins类化合物的构效关系, 阐明了B环上各取代基对抑制微管蛋白聚合活性的影响, 同时应用分子对接方法分析并验证了定量构效关系模型.     

苯并呋喃／噻吩联二苯类PTP1B抑制剂三维构效关系研究

主要采用比较分子力场分析方法（CoMFA）对苯并呋喃／噻吩联二苯类PTP1B (protein tyrosine phosphatase 1B)抑制剂进行了三维构效关系的研究，考察了 静电场、立体场和氢键场对构效关系的影响，交叉系数q^2的值达到0．58，表明 CoMFA得到的构效关系模型比较理想，同时test set中分子的预测活性也表明，模 型具有较好的预测能力，研究还表明，氢键场的加入不一定有利于模型的改善，通 过对分子场等值面图的分析，可以观察到叠合分子周围立体场和静电场对化合物活 性的影响，为改进原有化合物的结构，提高它们的活性提供了指导，还尝试采用比 较分子相似性指数分析方法（CoMFA）对这一系列化合物作了研究，结果表明虽然 CoMFA中加入了疏水场，但是对于研究的体系，CoMFA的模型质量并没有显著提高。     

一类吡唑衍生物的3D-QSAR研究

通过比较分子力场分析方法（CoMFA）和比较分子相似性指数分析方法 (CoMSIA),系统研究了30个2-烷基（烷硫基）-5-吡唑基-1,3,4-噁二唑（噻二? 颉⑷颍├嗷衔镆种扑疚瓶莶【锘钚缘娜酆隙抗剐Ч叵怠６杂? CoMFA,研究了不同移动步长对考虑静电场和立体场作用时构效关系的影响;对于 CoMSIA,研究了移动步长、场的组合、衰减因子α等参数变化对构效关系的影响, 发现当考虑立体场、疏水场、氢键受体场的贡献时能得到较好的结果。分别得到了 两种方法最为理想的3D-QSAR模型,所得三维等值线图为发现更高活性化合物提供 了有力的指导作用。     

皂甙的三维定量构效关系研究

针对目标分子柔性大的特点,在比较分子场分析(CoMFA)方法中采用交叉验证相关系数平方R^2引导的构象选择法。对12个皂甙分子的生物活性进行了三维定量构效关系研究。探讨了几种探针对构效关系结果的影响,并选择了一种较合理的“复合”探针方案。应用该复合探针构建CoMFA模型,发现影响药效的立体场与静电场的贡献分别为40%和40%,其它能量项的贡献为20%。该模型交叉验证的相关系数平方R^2为0.653,非交叉验证的R^2为0.991,方差比F(4,7)值130.195(即置信度99%以上),活性预计的标准偏差与极差比(s/△γ)为4.2%,表明模型具有较好的预测能力。根据该模型,预计在指定位置添加位阻较大的基团活性值提高将会比较明显。     

广谱沙粒病毒抑制剂的3D-QSAR分析

沙粒病毒(Arenaviruses)遍布全球,其中的拉沙热病毒可引起致命的拉沙热.通过应用比较分子场分析(CoMFA)和比较相似性指数分析法(CoMSIA)对47个广谱沙粒病毒抑制剂进行了三维定量构效关系(3D-QSAR)分析.使用立体场、静电场、疏水场和氢键受体场组合获得最优模型CoMSIA的统计结果为Q2=0.518,R2ncv=0.972,R2pre=0.911,说明该模型的可靠性和较好预测能力.此外,模型等势线图直观地解释了分子结构与其活性的关系,为进一步设计新型高效的沙粒病毒抑制剂提供了理论依据.     

二氢吡啶类化合物的三维定量构效关系

通过分子力学和量子化学计算,得出两种二氢吡啶衍生物的低能构象,再应用比较分子力场分析方法(CoMFA)和比较分子相似性指数分析方法(CoMSIA)分别对两种构象的43个二氢吡啶衍生物进行3D-QSAR研究. 计算结果表明,用两种方法建立的两种构象的构效关系模型均有较好的预测能力.通过分析CoMFA和CoMSIA的系数等势图,直观地了解二氢吡啶衍生物的结构对生物活性的影响,为进一步设计高活性的二氢吡啶衍生物提供一定的理论依据.     

应用分子图形学、分子力学、量子化学及静电势研究农药分子结构与性能关系(ΧⅢ)──用比较分子力场方法研究4-肟醚基喹唑啉类化合物的结构与抗烟草花叶病毒活性的三维构效关系

应用比较分子力场分析 (Co MFA)方法研究 4 -肟醚基喹唑啉类化合物抗烟草花叶病毒活性的三维构效关系 (3 D-QSAR) ,引入分子的摩尔折射率 (MR)和偶极矩 (DIPOLE)分别作为 Co MFA的第三和第四个场 .在此基础上进行偏最小二乘 (PLS)分析 :交叉验证 (leave-one-out)结果为 r2cv=0 .4 43 ,非交叉验证 (novalidation)结果为 r2 =0 .93 2 ,说明所建立的模型有较好的可靠性 ,并且在三维等值线图的基础上得到了一个此类化合物的模拟作用模型 ,据此可生长出一系列先导分子     

比较分子力场分析法(CoMFA)的研究新进展

CoMFA是目前3D-QSAR 中应用最广且最为成功的一种方法, 但其在设计思想和实施过程中还存在一定缺陷, 主要表现在匹配规则、新场引入、变量选择及数据处理等几个方面。本文对近年来关于CoMFA 方法的各种改进研究做了较为系统的综述, 并就CoMFA 在药物设计中的作用进行了展望。     

Comparison of 3D quantitative structure-activity relationship methods: Analysis of the in vitro antimalarial activity of 154 artemisinin analogues by hypothetical active-site lattice and comparative molecular field analysis

Two three-dimensional quantitative structure-activity relationship (3D-QSAR) methods, comparative molecular field analysis (CoMFA) and hypothetical active site lattice (HASL), were compared with respect to the analysis of a training set of 154 artemisinin analogues. Five models were created, including a complete HASL and two trimmed versions, as well as two CoMFA models (leave-one-out standard CoMFA and the guided-region selection protocol). Similar r2 and q2 values were obtained by each method, although some striking differences existed between CoMFA contour maps and the HASL output. Each of the four predictive models exhibited a similar ability to predict the activity of a test set of 23 artemisinin analogues, although some differences were noted as to which compounds were described well by either model.     

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。     

GA strategy for variable selection in QSAR studies: application of GA-based region selection to a 3D-QSAR study of acetylcholinesterase inhibitors

Comparative molecular field analysis (CoMFA) with partial least squares (PLS) is one of the most frequently used tools in three-dimensional quantitative structure-activity relationships (3D-QSAR) studies. Although many successful CoMFA applications have proved the value of this approach, there are some problems in its proper application. Especially, the inability of PLS to handle the low signal-to-noise ratio (sample-to-variable ratio) has attracted much attention from QSAR researchers as an exciting research target, and several variable selection methods have been proposed. More recently, we have developed a novel variable selection method for CoMFA modeling (GARGS: genetic algorithm-based region selection), and its utility has been demonstrated in the previous paper (Kimura, T., et al. J. Chem. Inf. Comput. Sci. 1998, 38, 276-282). The purpose of this study is to evaluate whether GARGS can pinpoint known molecular interactions in 3D space. We have used a published set of acetylcholinesterase (AChE) inhibitors as a test example. By applying GARGS to a data set of AChE inhibitors, several improved models with high internal prediction and low number of field variables were obtained. External validation was performed to select a final model among them. The coefficient contour maps of the final GARGS model were compared with the properties of the active site in AChE and the consistency between them was evaluated.     

Partial least squares modeling and genetic algorithm optimization in quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) studies based on chemometric techniques are reviewed. Partial least squares (PLS) is introduced as a novel robust method to replace classical methods such as multiple linear regression (MLR). Advantages of PLS compared to MLR are illustrated with typical applications. Genetic algorithm (GA) is a novel optimization technique which can be used as a search engine in variable selection. A novel hybrid approach comprising GA and PLS for variable selection developed in our group (GAPLS) is described. The more advanced method for comparative molecular field analysis (CoMFA) modeling called GA-based region selection (GARGS) is described as well. Applications of GAPLS and GARGS to QSAR and 3D-QSAR problems are shown with some representative examples. GA can be hybridized with nonlinear modeling methods such as artificial neural networks (ANN) for providing useful tools in chemometric and QSAR.     

Receptor-based modeling and 3D-QSAR for a quantitative production of the butyrylcholinesterase inhibitors based on genetic algorithm

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a series of structurally related steroidal alkaloids as butyrylcholinesterase (BuChE) inhibitors. Docking studies were employed to position the inhibitors into the BuChE active site to determine the most probable binding mode. The strategy was to explore multiple inhibitor conformations in producing a more reliable 3D-QSAR model. These multiple conformations were derived using the FlexS program. The conformation selection step for CoMFA was done by genetic algorithm. The genetic algorithm based CoMFA approach was found to be the best. Both CoMFA and CoMSIA yielded significant cross-validated q(2) values of 0.701 and 0.627 and the r(2) values of 0.979 and 0.982, respectively. These statistically significant models were validated by a test set of five compounds. Comparison of CoMFA and CoMSIA contour maps helped to identify structural requirements for the inhibitors and serves as a basis for the design of the next generation of the inhibitor analogues. The results demonstrate that the combination of ligand-based and receptor-based modeling with use of a genetic algorithm is a powerful approach to build 3D-QSAR models. These data can be used for the lead optimization process with respect to inhibition enhancement which is important for the drug discovery and development for Alzheimer's disease.     

CoMFA Analysis of Biodegradability

Abstract

CoMFA (comparative molecular field analysis) has been used to correlate the biodegradability of several classes of compounds. The technique uses an atomic probe to detect the steric and electrostatic fields around a molecule. Good cross-validated correlations were obtained for some series (alcohols, carboxylic acids and linear alkyl benzene sulphonates). Correlations were weaker for esters and benzene sulphonates, and were non-existent for phenols. These results may reflect the unreliability of biodegradation data, but may also be a result of molecular misalignment in the CoMFA procedure. CoMFA appears to be a potentially very useful method for the prediction of biodegradability.     

Insight into the structural requirements of narlaprevir-type inhibitors of NS3/NS4A protease based on HQSAR and molecular field analyses

In the life cycle of hepatitis C virus (HCV), NS3/NS4A protease has been proved to play a vital role in the replication of the HCV virus. Narlaprevir and its derivatives, the inhibitors of NS3/NS4A, would be potentially developed as important anti-HCV drugs in the future. In this study, quantitative structure-activity relationship (QSAR) analyses for 190 narlaprevir derivatives were conducted using comparative molecular field analysis (CoMFA), comparative molecular indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) techniques. Both of the best CoMFA and HQSAR models showed statistical significance for the training set and good predictive accuracy for the test set, which strongly manifested the robustness of the CoMFA and HQSAR models. The CoMFA contour maps and the HQSAR contribution maps were both presented. Furthermore, based on the essential factors for ligand binding derived from the QSAR models, sixteen new derivatives were designed and some of them showed higher inhibitory activities confirmed by our models and molecular docking studies. General speaking, this study provides useful suggestions for the design of potential anti-HCV drugs.