光系统Ⅱ抑制剂: 顺式氰基丙烯酸酯化合物的 三维构效关系的电子结构研究

以距离比较法所获得的顺式氰基丙烯酸酯化合物的活性构象为模板,对39个该类化合物采用比较分子场方法进行了三维构效关系的研究。结果表明,所获得的药效团模型具有很好的预测能力。同时采用量子化学的方法对活性构象模板分子电子结构作了讨论。     

光系统Ⅱ抑制剂-顺式氰基丙烯酸酯化合物的三维构效关系和电子结构研究

以距离比较法所获得的顺式氰基丙烯酸酯化合物的活性构象为模板,对39个该类化合物采用比较分子场方法进行了三维构效关系的研究.结果表明,所获得的药效团模型具有很好的预测能力.同时采用量子化学的方法对活性构象模板分子电子结构作了讨论.     

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。     

两类促肾上腺皮质释放激素(CRH)拮抗剂的分子场分析

采用比较分子相似性指数分析方法(CoMSIA)及比较分子场分析方法(CoMSIA)研究了两组CRH拮抗剂结构与活性的关系。在两种方法中,都考虑了静电场、立体场以及氢键场对构效关系的影响,结果表明采用CoMSIA得到构效关系模型要明显优于采用CoMFA得到的构效关系模型,在CoMSIA计算中,当引入疏水场时,三维构效关系模型能得到明显的改善,通过这个三维构效关系模型,可以较为精确地预测化合物的活性。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的立体、静电以及疏水特征对化合物活性的影响。     

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(DIStance COmparisons)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重叠模型的评估和新型PSII抑制剂的设计与合成.     

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.     

DATA类逆转录酶抑制剂的三维定量构效关系

采用对接方法得到HIV-1抑制剂DATA(二芳基三嗪类)分子的活性构象, 进一步用比较分子场分析(CoMFA)和比较分子相似性分析(CoMSIA)法对DATA类逆转录酶抑制剂(RTIs)的三维定量构效关系(3D-QSAR)进行了研究, 建立3D-QSAR模型, 以指导进一步结构修饰. 用此模型预测了5个DATA类似物, 预测偏差较小, 表明了所建立的模型具有较强的预测能力.     

比较分子场分析方法研究的最新进展*

比较分子场分析方法(CoM FA ) 是目前在三维定量构效关系(3D2Q SAR) 中应用最为广泛的方法之一。但传统的比较分子场分析方法在具体的实现过程中还存在着一些缺陷和不足, 包括分子场势能函数的选择, 活性构象的确定, 分子的叠合以及分子场变量的选取等等。本文结合我们科研组的工作, 就近几年CoM FA 方法的最新进展做了较为详细的阐述, 同时对CoM FA 的发展前景及其在药物设计中的应用进行了展望。     

促生长激素释放素三维定量构效关系及药效团模型

用比较分子场分析方法对促生长激素释放素Ｌ－６９２，４２９的系列物进行了三维定量构效研究，得到具有较强预测能力的模型并确定了母体的活性构象，用距离比较方法将Ｌ－６９２，４２９的活性构象与两个活性多肽进行了迭合，找到了可能的药效团，药效团模型显示Ｌ－６９２，４２９的氨基和四唑是氢键的给体和受体，而Ａ和Ｃ环是主要的疏水核心。     

光系统Ⅱ抑制剂—反式氰基丙烯酸酯比较分子场和电子结构研究

对反式氰基丙烯酸酯系列活性分子采用限制性系统搜索方法确定的药效团模型 ,与 9类不同骨架结构的光系统 抑制剂 DISCO模型中的反式氰基丙烯酸酯分子(M- 2 2 )的活性构象为模板所确定的药效团模型是非常相近的。对两种方法所获得的活性构象分子进行了 Co MFA研究 ,其结果是一致的。采用 PM3方法进行了量子化学计算 ,计算结果表明两种模型的构象分子具有相近的电子结构 ,根据分子静电场、立体场和电子结构探讨了该类抑制剂的构效关系。     

拟南芥乙酰羟基酸合成酶与磺酰脲的相互作用以及CoMFA研究

在分子水平上较为详尽地研究了85个磺酰脲类化合物与植物源野生型拟南芥AHAS酶的离体相互作用, 测定了这些化合物对AHAS酶的抑制常数Kiapp. 采用比较分子力场方法(CoMFA)对这些化合物与AHAS酶的相互作用进行了三维构效关系研究, 用此模型预测了检验组10个化合物的pKiapp值, 模型的预测结果与测试结果一致.     

嘧啶（氧）苯甲酸类除草剂的3D—QSAR研究

利用比较分子场分析 (Co MFA)方法 ,对 2 0种嘧啶 (氧 )苯甲酸类化合物进行了三维定量构效关系(3 D-QSAR)研究。得到了具有较强预测能力的 QSAR模型。并对此模型进行了验证 ,在此基础上 ,设计了具有更高活性的化合物。     

含氟新农药的构效关系研究Ⅰ.经典QSAR分析

为了能更深入地认识含氟新化合物作为农药的生物活性和其结构间的关系,建立有意义的构效关系模型,我们用经典QSAR(定量构效关系)方法研究了三十三个含氟化合物的两种不同的生物活性与结构的关系,其对抗黄瓜疫病活性模型有很好的解释能力和预测能力,并根据这个模型设计了一些新的活性结构.而对抑制西瓜白绢病的活性数据的处理未能获得理想模型.通过这一工作确立了先应用聚类等定性分析方法,再用多元统计分析方法作更深入研究的QSAR研究模式.     

咪唑-1-羟酸酯类化合物的构效关系研究

利用CoMFA方法，对20种咪唑-l-羧酸酯类化合物进行了三维定量均效关系研究．研究结果表明，影响其药效的主要因素是空间结构．得到了具有较强预测能力的QSAR模型．在此基础上，设计了高活性的化合物，并发现了分子中存在着明显的芳环堆积现象，可能和对受体的识别有关．     

Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands

The use of high throughput screening (HTS) to identify lead compounds has greatly challenged conventional quantitative structure-activity relationship (QSAR) techniques that typically correlate structural variations in similar compounds with continuous changes in biological activity. A new QSAR-like methodology that can correlate less quantitative assay data (i.e., "active" versus "inactive"), as initially generated by HTS, has been introduced. In the present study, we have, for the first time, applied this approach to a drug discovery problem; that is, the study of the estrogen receptor ligands. The binding affinities of 463 estrogen analogues were transformed into a binary data format, and a predictive binary QSAR model was derived using 410 estrogen analogues as a training set. The model was applied to predict the activity of 53 estrogen analogues not included in the training set. An overall accuracy of 94% was obtained.     

Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1 .     

Predicting physical-chemical properties of compounds from molecular structures by recursive neural networks

In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, Delta(solv)G degrees , has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the Delta(solv)G degrees in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R = 0.9985, standard deviation S = 0.68 kJ mol(-1), and mean absolute error MAE = 0.46 kJ mol(-1). The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.     

In silico studies toward the discovery of new anti-HIV nucleoside compounds through the use of TOPS-MODE and 2D/3D connectivity indices. 2. Purine derivatives

The TOPological Substructural MOlecular DEsign (TOPS-MODE) approach has been used to predict the anti-HIV activity in MT-4 assays (Estrada et al., 2002) of a diverse range of purine-based nucleosides. A database of 206 nucleosides has been selected from the literature and a theoretical virtual screening model has been developed. The model is able of discriminating between compounds that have anti-HIV activity and those that do not, with a good classification level of 85% in the training and 82.8% in the cross-validation series. On the basis of the information generated by the model, the correct classification of practically 80% of compounds from an external prediction set has been achieved using the theoretical model. Furthermore, the contribution of a range of molecular fragments to the pharmacological action has been calculated and this could provide a powerful tool in the design of nucleoside analogues that show activity against the HIV. Finally, a QSAR model has been developed that allows quantitative data to be obtained regarding the pharmacological potency shown by this type of compound.