氯代芳烃对孔雀鱼急性毒性的QSAR研究

采用密度泛函方法(DFT),在B3LYP/6-311G**水平上对37个氯代芳烃化合物进行几何全优化.以最高占用轨道能(EHOMO)、最低空轨道能(ELUMO)、最负氯原子的净电荷(Qcl)、与氯相连的碳原子的净电荷(Qc-cl、氢原子所带的最高正电荷(QH)、偶极距(μ)、苯环净电荷增量(△QR)和极化率(P)作为结构描述符,结合氯代芳烃对孔雀鱼的急性毒性数据,建立定量构效关系(QSAR)模型.采用逐步多元线性回归法,确定回归方程为-lgEC50=4.168-3.228QH+0.163μ-0.037P-3.493Qc-cl(n=28,R=0.950,SE=0.16802,F＝53.231).变异膨胀因子(VIF)和t检验法对模型的稳定性进行检验,预测集样本对模型的预测能力进行验证,结果显示所建模型具有良好的稳定性和较好的预测能力.此外,讨论了模型中结构参数对化合物毒性的影响,发现极化率对化合物的毒性影响最大.     

In silico modeling for the risk assessment of toxicity in cells

A cell is a complex biochemical reactor. Various biochemical reactions take place in it to carry out different tasks. One such task is the metabolism of the ubiquitous environmental carcinogenic compounds Polycyclic Aromatic Hydrocarbons (PAHs) in biological cells, which is crucial to model. These PAHs are lipophilic did partition into membranes and diffuse through them to demolish the DNA and thereby cause toxicity or tumors. Therefore there is a dire need of the development of a model for the assessment of these carcinogenic chemical compounds. Earlier, a 3D model was developed in order to investigate the cellular fate after being affected by PAHs, but this model was lacking the presence of Nucleolemma with its enzymatic reactions, which is an important factor to be considered. Thus, a new 3D model was developed which in addition to the other domains, consists of Nucleolemma along with its enzymatic reactions. A homogenization approach was used for the numerical treatment of cytoplasm to scale down the complexity of the model. The numerical results of the extended model were validated against the numerical results of the old model and the experimental results, where the results of extended model clearly show the improvement and convergence to the experimental results not only qualitatively but quantitatively as well.     

脂肪醇对番茄和红蜘蛛毒性的QSAR研究

应用量子化学HF/6-31G**从头算法得14种脂肪醇分子的优势构象,加上分子图形学技术,获得相应优化构象的电子结构参数和几何结构参数,并将这些参数与脂肪醇对番茄及红蜘蛛的毒性参数相关联.结果表明:脂肪醇对番茄的生物毒性与分子连接性拓扑指数(1Xv)、最负原子的静电荷(Q-)和分子极化率(α)相关性较好,而脂肪醇对红蜘蛛的生物毒性与分子连接性拓扑指数(1Xv)和分子最高占用轨道能(KH)相关性较好,建立脂肪醇对番茄和红蜘蛛毒性的构效关系式成功,找出影响生物毒性的主要结构因素.     

卤代酚对水生梨形四膜虫急锐毒性的QSAR研究

应用HyperChem7．O计算与卤代苯酚对水生四膜虫毒性相关的量化参数以及自己设计的结构编码参数,对31种卤代酚的毒性做QSAR研究。使用逐步回归筛选出影响卤代苯酚毒性的主要参数分子表面积S、分子体积ν和编码参数R3和R6建立QSAR方程。用留一法验证该模型,检验结果相关系数R为0．9496,标准偏差s为0．2606,与回归拟合得到的R=O．9686,s =0．2067十分接近,表明所建立的QSAR方程具有较好的预测效果和较高的预测稳定性。对QSAR方程各参数对卤代苯酚毒性的影响分析表明,S、ν影响卤代酚的基本毒性,R6影响极性麻醉,而R3则既与卤代酚基本毒性有关,也与极性麻醉有关。     

全局与局部模型对QSAR/QSPR预报能力比较

针对两组数据进行了比较讨论,试图说明在QSAR／QSPR研究中经常碰到的一个基本问题。第一组为一散布度(diver- sity)很大分子结构多样化的大样本数据;第二组则是按照分子结构相似度筛选出来的散布度较小结构相似的小样本数据。对于第一组数据,因数据集分散,全局模型难以完全描述物质结构特征与其性质之间的关系,所得回归结果很差(检验集相关系数Q2=0．68、平均预报偏差(RMSEP)=40．65)。试采用新近提出的局部懒惰回归(Local lazy regression,LLR)对其进行改善,但实际结果是局部模型的效果更差(Q2=0．60、RMSEP=45．05)。继对散布度较小且相对均匀(结构相似)的数据集用LLR方法建立局部模型,此时得到的预报结果(Q2=0．90、RMSEP=24．66)却明显优于全局模型(Q2=O．86、RMSEP=29．37)。     

Comparing the effectiveness of several modeling methods for fault prediction

We compare the effectiveness of four modeling methods—negative binomial regression, recursive partitioning, random forests and Bayesian additive regression trees—for predicting the files likely to contain the most faults for 28 to 35 releases of three large industrial software systems. Predictor variables included lines of code, file age, faults in the previous release, changes in the previous two releases, and programming language. To compare the effectiveness of the different models, we use two metrics—the percent of faults contained in the top 20% of files identified by the model, and a new, more general metric, the fault-percentile-average. The negative binomial regression and random forests models performed significantly better than recursive partitioning and Bayesian additive regression trees, as assessed by either of the metrics. For each of the three systems, the negative binomial and random forests models identified 20% of the files in each release that contained an average of 76% to 94% of the faults.     

几种QSAR建模方法在化学中的应用与研究进展

定量构效关系(quantitative structure activity relationship,QSAR)是对化合物结构与其活性之间关系的定量描述研究,它是目前国际上1个比较活跃的研究领域。定量构效关系研究最初应用在生物领域,是定量药物设计的1个研究分支领域,为了适应合理设计生物活性分子的需要而发展起来的。由于计算机技术的发展和应用,QSAR的研究提高到了1个新的水平,日益成熟。QSAg的研究对象包括化合物的各种生物活性、毒性、药物的各种药代动力学参数和生物利用度以及分子的各种物理化学性质和环境行为等,研究领域涉及化学、药物以及环境等诸多学科。本文简单介绍了几种2D-QSAR和3D-QSAR建模方法:多元线性回归(MLR)、主成分分析(PCA)、偏最小二乘法(PLS)、人工神经网络(ANN)、支持向量机(SVM)、比较分子力场分析方法(CoMFA)和比较分子相似性指数(CoMSLA),并对这几种不同的建模方法在实际中的应用进行举例。综述了定量构效关系在环境化学、农业化学、药物化学中的应用,并对定量构效关系研究的发展趋势进行了展望。