基于基因表达式编程预测醛类化合物急性毒性

对大鼠急性毒性的定量构效关系模型提出了一种新的算法:基因表达式编程,其核心是种群的进化.种群由染色体构成,染色体是由结构正确的基因组合而成,包含头部和尾部,对种群中染色体的基因进行特定的操作便形成了进化.本实验以启发式方法筛选的8个关键描述符为建模参数,应用基因表达式编程建立了88种醛类化合物分子结构对大鼠急性毒性的定量构关系模型,模型交互检验相关系数为0.947,均方误差为0.037.通过与支持向量机方法的比较表明:基因表达式编程建立的定量构效关系模型能够更好地预测醛类化合物对大鼠急性毒性的半效致死量,且具有较强的稳定性.     

3D-QSAR Analysis of Naphthyltriazole(Lesinurad) Analogs as Potent Inhibitors of Urate Transporter 1

To obtain useful information for identifying inhibitors of urate transporter 1(URAT1), three-dimensional quantitative structure-activity relationship(3 D-QSAR) analysis was conducted for a series of lesinurad analogs via Topomer comparative molecular field analysis(CoMFA). A 3 D-QSAR model was established using a training set of 51 compounds and externally validated with a test set of 17 compounds. The Topomer CoMFA model obtained(q^2 = 0.976, r2 = 0.990) was robust and satisfactory. Subsequently, seven compounds with significant URAT1 inhibitory activity were designed according to the contour maps produced by the Topomer CoMFA model.     

启发式方法研究有机化合物在全二维气相色谱中的定量结构-保留关系

采用半经验量子化学PM3的方法计算出130个有机化合物的描述符,用启发式方法分别对化合物在全二维气相色谱的3支色谱柱上的保留值建立了相应的定量结构-保留相关模型,并对模型进行了检验。所建模型呈现较好的线性,相关系数的平方(R2)均大于0.88,标准偏差（S）均小于0.105,留一法交互检验的相关系数的平方(R2cv)与所建模型的R2相当,说明模型具有良好的稳定性。化合物的预测结果显示所建模型有较准确的预测能力。     

Quantitative Structure�CRetention Relationship Study on Nitrogen-Containing Polycyclic Aromatic Compounds by Using Molecular Electronegativity Interaction Vector

The molecular structures of 117 nitrogen-containing polycyclic aromatic compounds (N-PACs) were described by a method of molecular structural characterization (MSC) called molecular electronegativity interaction vector (MEIV). The samples were divided into a training set and a test set. For the training set, a quantitative structure?Cretention relationship (QSRR) model was built up by multiple linear regression (MLR) and the model was evaluated by performing the cross validation with the leave-one-out (LOO) procedure. The correlation coefficient (R) and the cross-verification correlation coefficient (R _CV) of the model were 0.992 and 0.991, respectively. Moreover, the model was evaluated by the test set and satisfactory results with a correlation coefficient (R _test) of 0.993 were obtained. The results suggested good stability and predictability of the model.     

Prediction of quantitative calibration factors of some organic compounds in gas chromatography

A quantitative structure-property relationship (QSPR) methodology that involves multilinear (Hansch-type) and nonlinear (radial basis function neural network (RBFNN)) approaches was performed to correlate the quantitative molar calibration factors (f(M)) of 140 organic compounds against structural factors. The statistical characteristics provided by the multiple linear model (R(2) = 0.963; RMS = 0.089; AARD = 3.86% for test set) indicated satisfactory stability and predictive ability, while the predictive ability of the RBFNN model is somewhat superior (R(2) = 0.983; RMS = 0.075; AARD = 3.19% for test set). The multilinear model provided some insight into the main structure factors that modulate the quantitative calibration factor of the investigated compounds.     

Tomocomd-Cardd, a novel approach for computer-aided ‘ rational’ drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds

Summary In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified 90.37% of compounds in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. The QSAR model correctly classified 88.18% of compounds in this external prediction set. A second model was performed to outline some conclusions about the possible modes of action of anthelmintic drugs. This model permits the correct classification of 94.52% of compounds in the training set, and 80.00% of good global classification in the external prediction set. After that, the developed model was used in virtual in silicoscreening and several compounds from the Merck Index, Negwers handbook and Goodman and Gilman were identified by models as anthelmintic. Finally, the experimental assay of one organic chemical (G-1) by an in vivo test coincides fairly well (100) with model predictions. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.     

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.     

A partial least squares regression study with antioxidant flavonoid compounds

The quantitative structure-activity relationship of a set of 19 flavonoid compounds presenting antioxidant activity was studied by means of PLS (Partial Least Squares) regression. The optimization of the structures and calculation of electronic properties were done by using the semiempirical method AM1. A reliable model (r ²=0.806 and q ²=0.730) was obtained and from this model it was possible to consider some aspects of the structure of the flavonoid compounds studied that are related with their free radical scavenging ability. The quality of the PLS model obtained in this work indicates that it can be used in order to design new flavonoid compounds that present ability to scavenge free radicals.     

基于启发式方法和支持向量机方法预测有机物的热导率

构建147个有机物分子结构与其热导率值之间的定量结构-性质关系(QSPR)模型, 探讨影响有机物热导率的结构因素. 以147个化合物作为样本集, 随机选择118个作为训练集, 29个作为测试集. 应用CODESSA软件计算了组成、拓扑、几何、静电和量子化学等描述符, 通过启发式方法(HM)筛选得到5个结构参数并建立线性回归模型; 用所选5个结构参数作为支持向量机(SVM)的输入, 建立非线性的支持向量机回归模型. 预测结果表明: 支持向量机回归模型的性能(复相关系数R²=0.9240)虽略低于启发式回归模型的性能(R²=0.9267), 但是支持向量机方法预测性能(R²=0.9682)高于启发式方法的预测性能(R²=0.9574), 对于QSPR模型来说, 预测性能更重要. 因此, 总体来说支持向量机方法优于启发式方法. 支持向量机方法和启发式方法的提出为工程上提供了一种根据分子结构预测有机物热导率的新方法.     

3D-QSAR Studies on a Series of Indoleamide Derivatives as Antiplasmodial Drugs

In this study, three-dimensional quantitative structure-activity relationship(3D-QSAR) was studied for the antiplasmodial activity of a series of novel indoleamide derivatives by comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(Co MSIA). 3D-QSAR model was established by a training set of 20 compounds and was externally validated by a test set of 4 compounds. The best prediction(Q~2 = 0.593 and 0.527, R~2 = 0.990 and 0.953, r_(pred)~2 = 0.967 and 0.962 for CoMFA and CoMSIA) was obtained according to CoMFA and CoMSIA. Those parameters indicated the model was reliable and predictable. We designed several molecules with high activities according to the contour maps produced by the CoMFA and CoMSIA models.