QSAR analysis of 1,3-diaryl-2-propen-1-ones and their indole analogs for designing potent antibacterial agents

A series of 1,3-diaryl-2-propen-1-ones and their indole analogs were synthesized and evaluated for antibacterial activity. Structures of newly synthesized compounds were confirmed by physicochemical, spectral and elemental analysis. All the compounds were screened for their antibacterial activities against four different bacterial strains. The QSAR studies were performed using Vlife MDS 3.5 software. QSAR equation revealed that selected electronic, steric and lipophilic parameters have good correlation with antibacterial activity. Best equations were selected on basis of the correlation coefficient (r ²) and the predictable ability of the equations. The present findings suggest that the 1,3-diaryl-2-propen-1-ones framework is an attractive template for structure optimization to achieve higher potency, lower toxicity, and a wider spectrum of antibacterial activity.     

Cytotoxicity and 2D-QSAR study of some heterocyclic compounds

Herein we have studied the cytotoxicity and quantitative structure–activity relationship (QSAR) of heterocyclic compounds containing cyclic urea and thiourea nuclei. A set of 22 hydantoin and thiohydantoin related heterocyclic compounds were investigated with respect to their LC₅₀ values (Log of LC₅₀) against brine shrimp lethality bioassay in order to derive the 2D-QSAR models using MLR, PLS and ANN methods. The best predictive models by MLR, PLS and ANN methods gave highly significant square correlation coefficient (R²) values of 0.83, 0.81 and 0.91 respectively. The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient Q² (0.74).     

Combined 2D and 3D-QSAR,molecular modelling and docking studies of pyrazolodiazepinones as novel phosphodiesterase 2 inhibitors

Selective inhibition of phosphodiesterase 2 (PDE2) in cells where it is located elevates cyclic guanosine monophosphate (cGMP) and acts as novel analgesic with antinociceptive activity. Three-dimensional quantitative structure–activity relationship (QSAR) studies for pyrazolodiazepinone inhibitors exhibiting PDE2 inhibition were performed using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and Topomer CoMFA, and two-dimensional QSAR study was performed using a Hologram QSAR (HQSAR) method. QSAR models were generated using training set of 23 compounds and were validated using test set of nine compounds. The optimum partial least squares (PLS) for CoMFA-Focusing, CoMSIA-SDH, Topomer CoMFA and HQSAR models exhibited good ‘leave-one-out’ cross validated correlation coefficient (q²) of 0.790, 0.769, 0.840 and 0.787, coefficient of determination (r²) of 0.999, 0.964, 0.979 and 0.980, and high predictive power (r²_pred) of 0.796, 0.833, 0.820 and 0.803 respectively. Docking studies revealed that those inhibitors able to bind to amino acid Gln859 by cGMP binding orientation called ‘glutamine-switch’, and also bind to the hydrophobic clamp of PDE2 isoform, could possess high selectivity for PDE2. From the results of all the studies, structure–activity relationships and structural requirements for binding to active site of PDE2 were established which provide useful guidance for the design and future synthesis of potent PDE2 inhibitors.     

Receptor binding affinity based comparative QSAR study of testosterone derivatives

The quantitative structure activity relationship models of 22 testosterone derivatives have been made with the help of topological and quantum chemical parameters. The molecular modeling and geometry optimization have been carried out with CAChe Pro software. The calculations of topological and quantum chemical parameters have been done by MOPAC 2007. The statistical parameters are calculated by STATISTICA and SSP software. The study indicates that the topological parameters better predict the receptor binding affinity of testosterone derivatives, whereas quantum chemical parameters better predict androgenic potency of testosterone derivatives as indicated by correlation coefficient, standard error, standard error of estimation, p value, t value, and degree of freedom of the quantitative structure activity relationship (QSAR) models. The predicted activity values obtained by these QSAR models are close to observed activity. © 2011 Wiley Periodicals, Inc. Int J Quantum Chem, 2012     

Structure-Toxicity Relationships for Aminoalkanols: A Comparison with Alkanols and Alkanamines

Abstract

The relative toxicity (logIGC^?1 ₅₀) of 49 selected aliphatic amines and aminoalkanols was evaluated in the static Tetrahymena pyriformis population growth impairment assay. Excess toxicity, indicated by potency greater than predicted for non-polar narcotic alkanols, was associated with both classes of test chemicals. Moreover, the aminoalkanols were found to be more toxic than the corresponding alkanamines. A high quality 1-octanol/water partition coefficient (log K _ow) dependent quantitative structure-activity relationship (QSAR), logIGC^?1 ₅₀ = 0.78 (log K _ow)-1.42; r ² = 0.934, was developed for alkanamines. This QSAR represented the amine narcosis mechanism of toxic action. No quality QSAR was developed for the aminoalkanols. However, several structure-toxicity features were observed for this class of chemicals. Two-amino-1-hydroxy derivatives being more toxic than the corresponding derivatives, where the amino and hydroxy moieties were separated by methylene groups. Hydrocarbon branching next to the amino moiety resulted in decreased toxicity. Aminoalkanol alters lipid metabolism in T. pyriformis.     

Rational selection of training and test sets for the development of validated QSAR models

Quantitative Structure–Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors (kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R² (q²) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q ² for the training set and accuracy of prediction (R ²) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.     

脂质体电动色谱用于预测皮肤渗透性的定量保留活性关系的建立

脂质体电动色谱 （Liposome electrokinetic chromatography,LEKC）是一种简单快速的评价药物与生物膜相互作用的方法。本文建立了脂质体电动色谱作为高通量筛选皮肤渗透性的体外分析方法。将脂质体电动色谱中保留因子的对数值（log k）作为自变量建立了定量保留活性关系式。采用SPSS分析软件对于16种结构不同的化合物进行分析,结果表明log k与皮肤渗透性常数线性相关性良好( R²=0.886)。采用交互验证评价了该模型的预测能力。在定量保留活性关系中的一个变量和传统定量构效关系中的三个变量可解释的能力( R² =0.704)相似。文中建立的定量保留活性关系模型对于新化合物早期的筛选可提供一种有效快捷的方法。