pKa prediction for acidic phosphorus‐containing compounds using multiple linear regression with computational descriptors

Ninety‐six acidic phosphorus‐containing molecules with pK_a 1.88 to 6.26 were collected and divided into training and test sets by random sampling. Structural parameters were obtained by density functional theory calculation of the molecules. The relationship between the experimental pK_a values and structural parameters was obtained by multiple linear regression fitting for the training set, and tested with the test set; the R² values were 0.974 and 0.966 for the training and test sets, respectively. This regression equation, which quantitatively describes the influence of structural parameters on pK_a, and can be used to predict pK_a values of similar structures, is significant for the design of new acidic phosphorus‐containing extractants. © 2016 Wiley Periodicals, Inc.     

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification

Previous modelling of the median lethal dose (oral rat LD₅₀) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discriminant analysis (LDA) based-approach was utilized to assign indicators such as the pesticide target species, mode of action, or target species - mode of action combination. LDA models were able to predict these indicators with about 87% accuracy. Toxicity is predicted utilizing the QSAR model fit to chemicals with that indicator. Toxicity was also predicted using a global hierarchical clustering (HC) approach which divides data set into clusters based on molecular similarity. At a comparable prediction coverage (~94%), the global HC method yielded slightly higher prediction accuracy (r² = 0.50) than the LDA method (r² ~ 0.47). A single model fit to the entire training set yielded the poorest results (r² = 0.38), indicating that there is an advantage to clustering the dataset to predict acute toxicity. Finally, this study shows that whilst dividing the training set into subsets (i.e. clusters) improves prediction accuracy, it may not matter which method (expert based or purely machine learning) is used to divide the dataset into subsets.     

QSAR modeling for lipid peroxidation inhibition potential of flavonoids using topological and structural parameters

In the present study, Quantitative Structure-Activity Relationship (QSAR) modeling has been carried out for lipid peroxidation (LPO)-inhibition potential of a set of 27 flavonoids, using structural and topological parameters. For the development of models, three methods were used: (1) stepwise regression, (2) factor analysis followed by multiple linear regressions (FA-MLR) and (3) partial least squares (PLS) analysis. The best equation was obtained from stepwise regression analysis (Q² = 0.626) considering the leave-oneout prediction statistics.      

Synthesis,Bioactivities and Structure Activity Relationship of N‐4‐Methyl‐1,2,3‐thiadiazole‐5‐carbonyl‐N′‐phenyl Ureas

Twenty nine novel N‐4‐methyl‐1,2,3‐thiadiazole‐5‐carbonyl‐N′‐phenyl ureas were designed and synthesized, and their structures were confirmed by proton nuclear magnetic resonance (¹H NMR), infra red spectroscopy (IR) and high‐resolution mass spectroscopy (HRMS). Compounds V‐9 , V‐11 , V‐12 , V‐15 , V‐19 , V‐21 , V‐22 and V‐24 exhibit excellent activity against Culex pipiens pallens. Compounds V‐12 and V‐22 present good insecticidal activity against Plutella xylostella L. Their median lethal concentrations (LC₅₀) are 164.15 and 89.69 mg·L^?1, respectively. Compound V‐11 also has potential wide spectrum of fungicide activity. Its median effective concentrations (EC₅₀) detected from 3.82 µg·mL^?1 against Physalospora piricola to 31.60 µg·mL^?1 against Cercospora arachidicola. Compounds V‐15 and V‐24 show outstanding induction activities as same as positive controls TDL and ningnanmycin, furthermore V‐24 has the highest induction activity of 41.85%±4.43%. To elucidate the structure activity relationship in these compounds, a 3D‐QSAR model has been built. The established model showed a reliable predicting ability with q² values of 0.643 and r² values of 0.982.     

Three-dimensional Quantitative Structure-Activity Relationship Analyses of a Series of Butenolide ETA Antagonists

Two kinds of Three-dimensional Quantitative Structure-activity Relationship(3D-QSAR) methods,comparative molecular filed analysis(CoMFA) and comparative molecular similarity indices analysis (CoMSIA) ,were applied to analyze the structure-activity relationship of a series of 63 butenolide ETA selective antagonists with respect to their inhibition against human ETA receptor,The CoMFA and CoMSIA models were developed for the conceivable alignment of the molecules based on a template structure from the crystallized data.The statistical results from the initial orientation of the aligned molecules show that the 3D-QSAR model from CoMFA(q^2=0.543) is obviously superior to that from the conventional CoMSIA(q^2=0.407).In order to refine the model,all-space search (ASS) was applied to minimize the field sampling process.By rotating and translating the molecular aggregate within the grid systematically,all the possible samplings of the molecular fields were tested and subsequently the one with the highest q^2 was picked out .The comparison of the sensitivity of CoMFA and CoMSIA to different space orientation shows that the CoMFA q^2 values are more sensitive to the translations and rotations of the aligned molecules with respect to the lattice than those of CoMSIA.The best CoMFA model from ASS was further refined by the region focused technique.The high quality of the best model is indicated by the high corss-validated correlation and the prediction on the external test set.The CoMFA coefficient contour plots identify several key features that explain the wide range of activities,which may help us to design new effective ETA selective antagonists.