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In the present work, QSAR (quantitative structure−activity relationship) analysis has been executed for RG7834 analogs. RG7834 is a first-in-class selective and orally available dihydroquinolizinone (DHQ)-based small molecule Hepatitis B virus expression inhibitor. OECD's guidelines have been followed for developing multiple QSAR models for Hepatitis B virus expression inhibitory activity of 73 RG7834 analogs. The present multiple QSAR models are not only easily interpretable but possess high external predictive ability, as well. These are effective in the recognition of many privileged and underprivileged molecular descriptors, which could be very valuable for the use of these models by the experts and nonexperts of QSAR in future optimizations. The models satisfy threshold values for many fitting, internal and external validation parameters, such as R2 = 0.83, Q2 = 0.80, CCCext = 0.88, etc., thereby demonstrating good external predictive ability of the models. The multiple QSAR and pharmacophoric models successfully identified a good number of important positively and negatively related structural features of RG7834 analogs that govern their Hepatitis B virus expression inhibitory activity. The results could be very beneficial to synthetic/medicinal chemists for future alterations of RG7834 analogs as better drug candidates. [ABSTRACT FROM AUTHOR]

In the present work, quantitative structure-activity relationship and pharmacophore modeling analysis were performed for human African trypanosomiasis healing activity of pyridyl benzamides (dataset-1) and 3-(oxazolo[4,5-b]pyridin-2-yl)anilides (dataset-2). For quantitative structure-activity relationship analysis, a pool of descriptors (mono-dimensional to three-dimensional) was generated, followed by descriptor reduction using objective and subjective feature selection. Multiple splitting was employed for the generation of multiple quantitative structure-activity relationship models to get maximum information about the descriptors that have correlation with the HAT activity of pyridyl benzamides and 3-(oxazolo[4,5-b]pyridin-2-yl)anilides. The genetic algorithm-multilinear regression quantitative structure-activity relationship models have excellent statistical robustness with good external predictive ability. The pharmacophore model and quantitative structure-activity relationship analyses furnished complementary and consensus results to each other. [ABSTRACT FROM AUTHOR]

Quantitative Structure-Activity Relationship not only provides guidelines regarding structural features responsible for biological activity but it can be used also for prediction of desired activity prior to synthesis of untested chemicals. Therefore, an appropriate validation of any QSAR is of utmost importance to judge its external predictive ability. Generally, internal and external validations (preferred by many) are used in the absence of a true external dataset. The model developed using external method may not be reliable as it may not capture all essential features required for the particular SAR due to omission of some compounds, especially for small datasets. In external validation, the splitting is done either rationally or in random manner before descriptor selection. In the present study, rational splitting of dataset was performed using a novel method and its effect on statistical parameters was analyzed. The analysis reveals that the predictive ability of a QSAR model is sensitive toward (1) the method of splitting and (2) distribution of the training and the prediction sets. In addition, purposeful selection can be used to influence the statistical parameters; therefore, external validation based on single split is insufficient to guarantee the true predictive ability of a QSAR model. Besides, it appears that the selection of descriptors prior to splitting (information leakage) has little role to play in deciding external predictivity of the model. The present study reveals that as many as possible statistical parameters should be examined along with boot-strapping instead of single external validation. [ABSTRACT FROM AUTHOR]

Tautomerism is an important aspect associated with a variety of pharmacologically and biologically active compounds. It is a challenge to account for tautomerism in computer-aided drug designing (CADD). The estimations and calculations of many physico-chemical properties and theoretical descriptors of the molecules are sensitive to tautomerism. In this study, we have attempted to analyze the effect of tautomerism on feature selection and statistical performance/characteristics of conventional quantitative structure-activity relationship (QSAR) equations. These equations are developed using 2D and 3D-descriptors employing two different statistical methods, i.e., genetic algorithm (GA) and stepwise regression (SR). Five datasets of moderate sizes viz. (1) anti-malarial activity of synthetic prodiginines against multi-drug resistant strain ( N = 43), (2) anti-malarial activity of bisaryl quinolones ( N = 37), (3) anti-malarial activity of phosphoramidate and phosphorothioamidate analogs of amiprophos methyl ( N = 36), (4) anti-proliferative activity of substituted N-phenyl ureidobenzenesulfonate derivatives ( N = 44), and (5) anti-HIV activity of indolylarylsulfones as HIV-1 non-nucleoside reverse transcriptase inhibitors ( N = 36) showing different types of tautomerism were used in the study. In each case, the developed model and the selected descriptors derived using one tautomer were applied on other tautomeric forms to understand the influence of tautomerism on QSAR equations. Different parameters like R, R, R, R, F, S and Y-randomization were used for thorough validation of all the models. The results revealed that tautomerism has significant influence on feature selection. In addition, it was found that tautomerism has a great influence on the performance of QSAR models of the second and the third datasets. However, no significant influence was observed on the statistical characteristics of QSAR models for datasets 1, 4, and 5. Therefore, it is suggested that separate models need to be developed for different tautomeric forms of a dataset. [ABSTRACT FROM AUTHOR]

In the present study, we have carried out extensive GUSAR and conventional 3D QSAR analyses of 49 synthetic prodiginines possessing moderate to high activities against multi drug resistant strain of Plasmodium falciparum. 2D and 3D descriptors, various statistical parameters, viz. R, R, standard error, Y-randomization, etc., were checked to build successful QSAR model. The best four parametric GA-MLR 3D-QSAR model was found to have R = 0.84; R = 0.83. GUSAR analysis was performed to vindicate the QSAR results and get additional results. The consensus GUSAR model based on QNA descriptor is found to have R = 0.80 and Q = 0.76. The analyses reveal that certain groups/atoms like -F, benzylic -CH- and -OCH play crucial role in deciding the antimalarial activity of prodiginines. The analyses could be useful to improve the antimalarial activity of these biologically privileged molecules. [ABSTRACT FROM AUTHOR]

Polyphenols and their derivates have been reported to exhibit inhibitory activity against α-glucosidase. The relationship between structure and inhibitory activity of polyphenols was studied by means of multiple linear regression analysis with use of various descriptors derived from the 2D and 3D representations of molecules and physicochemical parameters calculated by DRAGON. The best model for the prediction of inhibitory activity was obtained using combination of topological charge index (JGI2), information index (CIC2) and number of exo-conjugated C atoms ( nCconjR). This study revealed that an enhanced inhibitory activity of polyphenols is mainly conditioned by stabilization of molecule due to intramolecular electron and charge delocalization. [ABSTRACT FROM AUTHOR]

This work describes the quantiative structure–activity relationship (QSAR) study of lipid peroxidation inhibitory effect of catechins, anthocyanidins and anthocyanins using molecular descriptors and physicochemical parameters derived from optimised three-dimensional (3D) structure, since this set of studied compounds contains stereoisomers with different activities. Six groups of 3D descriptors have been used to generate QSAR models: geometrical, 3D molecule representation of structures based on electron diffraction (3D-MoRSE); Randic molecular profiles; geometry, topology and atom weights assembly (GETAWAY); radial distribution function (RDF); and weigthed covariance matrices (WHIM) descriptors. The 3D molecular descriptors and physicochemical parameters have been calculated applying the online software Parameter Client and HyperChem 8.0. The primary selection of 3D molecular descriptors and physicochemical parameters was based on their ability to discriminate stereoisomers. Further selection of predictor variables for multiple regression was performed by the best-subset and forward stepwise method. The best-developed QSAR models consisted of geometrical, RDF and Randic molecular profiles descriptors. Those descriptors could be used for the prediction of the biological activity of catechin stereoisomers and their derivates. The obtained models suggest that the inhibitory effect of studied compounds is related to the shape of the molecule and the three-dimensional distribution of atomic mass in the molecule. [ABSTRACT FROM AUTHOR]

Three new series of mixed-ligand clusters of the [(M6X12)X2(RCN)4] (M=Nb, Ta; X=Cl, Br; R=Et, n-Pr, n-Bu) composition have been prepared. It is supposed that four nitrile molecules and two halogen atoms are coordinated to the terminal octahedral coordination sites of the [M6X12]2+ unit. [ABSTRACT FROM AUTHOR]