Approach on quantitative structure-activity relationship for design of a pH neutral carrier containing tertiary amino group

The quantitative structure-activity relationship (QSAR) for neutral carriers used to prepare hydrogen ion sensors has been studied. A series of synthesized carrier compounds were taken as the training set. Five molecular structure parameters of the compounds were calculated by using CNDO/2 algorithm and used as feature variables in constructing QSAR model. The lower and upper limits of the linear pH response range were taken as the activity measure. The corresponding model equations were derived from the stepwise regression procedure. With the established QSAR model, a new pH carrier, (4-hydroxybenzyl) didodecylamine (XIII) was proposed and synthesized. The PVC membrane pH electrode based on carrier XIII with a wide pH linear response range of 2.0-12.5 was prepared. Having a theoretical Nernstian response slope of 57.2 ± 0.3 mV/pH (n = 5 at 25 °C) without a super-Nernstian phenomenon, the sensor had low resistance, short response time, high selectivity and good reproducibility. Moreover, the sensor was successfully applied to detecting the pH value of serum samples.     

酪氨酸蛋白激酶抑制剂:亚苄基丙二腈类衍生物定量构效关系的量子化学研究

用量子化学从头算方法,对亚苄丙二腈衍生物的定量构效关系(QSAR)进行了研究,结果显示:指示变量I,偶极矩Dipole和最低空轨道的能量ELUMO对亚苄丙二腈衍生物的抑制活性有很大影响,回归分析得到了相关性好的相关模型(R=0.95).I=0时,亚苄基丙二腈类衍生物的抑制作用由其化学反应性决定;I=1时其抑制作用由其静电性决定.     

Quantitative weight of evidence method for combining predictions of quantitative structure-activity relationship models

ABSTRACT

A method for combining statistical-based QSAR predictions of two or more binary classification models is presented. It was assumed that all models were independent. This facilitated the combination of positive and negative predictions using a quantitative weight of evidence (qWoE) procedure based on Bayesian statistics and the additivity of the logarithms of the likelihood ratios. Previous studies combined more than one prediction but used arbitrary strengths for positive and negative predictions. In our approach, the combined models were validated by determining the sensitivity and specificity values, which are performance metrics that are a point of departure for obtaining values that measure the weight of evidence of positive and negative predictions. The developed method was experimentally applied in the prediction of Ames mutagenicity. The method achieved a similar accuracy to that of the experimental Ames test for this endpoint when the overall prediction was determined using a combination of the individual predictions of more than one model. Calculating the qWoE value would reduce the requirement for expert knowledge and decrease the subjectivity of the prediction. This method could be applied to other endpoints such as developmental toxicity and skin sensitisation with binary classification models.     

A quantitative structure-activity relationship study of antifungal analogues of 3,4-substituted 5-((1H-1, 2, 4-triazol-1-yl)methyl)-4H-1,2,4-triazole

A series of 5-((1H-1, 2, 4-triazol-1-yl)methyl)-4H-1,2,4-triazole derivatives were prepared and evaluated for their antifungal activities. Quantitative structure–activity relationship studies were performed on these compounds using physicochemical parameters as independent parameters and antifungal activity as a dependent parameter, where antifungal activity correlated best (correlation coefficient r > 0.8) with physicochemical parameters (Hammett’s constants σ _p , F) and van der Waals volume V ₁. Results are interpreted on the basis of multiple regression and cross-validation methodology. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Chemical structures of A1–26 and A39; synthetic procedures; synthesis schemes; mps, yields, ¹H NMR; elemental analyses of compounds and single crystal X-ray diffraction of compounds.     

Extraction,separation and quantitative structure–retention relationship modeling of essential oils in three herbs

The essential oils extracted from three kinds of herbs were separated by a 5% phenylmethyl silicone (DB‐5MS) bonded phase fused‐silica capillary column and identified by MS. Seventy‐four of the compounds identified were selected as origin data, and their chemical structure and gas chromatographic retention times (RT) were performed to build a quantitative structure–retention relationship model by genetic algorithm and multiple linear regressions analysis. The predictive ability of the model was verified by internal validation (leave‐one‐out, fivefold, cross‐validation and Y‐scrambling). As for external validation, the model was also applied to predict the gas chromatographic RT of the 14 volatile compounds not used for model development from essential oil of Radix angelicae sinensis. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validations indicated that the best quantitative structure–retention relationship model was robust and satisfactory, could provide a feasible and effective tool for predicting the gas chromatographic RT of volatile compounds and could be also applied to help in identifying the compound with the same gas chromatographic RT.     

Integration of genetic virtual screening patterns and latent multivariate modeling techniques for QSAR optimization based on combinations and/or interactions between peptides and proteins

Both the concept and the model of snug quantitative structure-activity relationship (QSAR) were pro-posed and developed for molecular design through constructing QSAR based on some known mode of receptor/ligand interactions. Many disadvantages of traditional models can be avoided by using the proposed method because the traditional models only determined upon molecular structural features in sample sets themselves. A genetic virtual screening of peptide/protein combinations (GVSPPC) is proposed for the first time by utilizing this idea to examine peptide/protein affinity activities. A genetic algorithm (GA) was developed for screening combinative targets with an interaction mode for virtual receptors. GVSPPC succeeds in disposing difficulties in rational QSAR,in order to search for the ligand/receptor interactions on conditions of unknown structures. Some bioactive oligo-/poly-peptide systems covering 58 angiotensin converting enzyme (ACE) inhibitors and 18 double site mutation residues in camel antibody protein cAb-Lys3 were investigated by GVSPPC with satisfactory results (R 2 cu>0.91,Q 2 cv > 0.86,ERMS=0.19-0.95),respectively,which demonstrates that GVSPPC is more inter-pretable in the ligand-receptor interaction than the traditional QSAR method.     

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.