QSAR studies were performed to understand the structure activity relationship (SAR) and to build the computational model to predict newer inhibitors with improved potency. In this study, a library of thiophene-anthranilamide based inhibitors of factor Xa was used to develop QSAR model. The library was divided into two sets: Training and Test sets. QSAR Model consists of four descriptors with R-...

A three-dimensional quantitative structure activity relationship (3D-QSAR) study using comparative molecular field analysis (CoMFA) method was performed on 2,5-disubstituted1,3,4-thiadiazole derivatives as diuretic agents. This study was performed using 40 compounds, in which the CoMFA model was developed using a training set of 30 compounds. Ten compounds (selected at randomly served as a test...

although extensive experimental work has been carried out during the last several years, experimental reaction rate constants are available only for hundreds of compounds. therefore, it is useful to develop a theoretical prediction method, which can be used to obtain estimates of the necessary kinetic parameters. one of the most successful approaches to predict chemical properties starting only...

Andrographolide derivatives were shown to inhibit alpha-glucosidase. To investigate the relationship between activities and structures of andrographolide derivatives, a training set was chosen from 25 andrographolide derivatives by the principal component analysis (PCA) method, and a quantitative structure-activity relationship (QSAR) was established by 2D and 3D QSAR methods. The cross-validat...

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibilit...

quantitative structure-activity relationship (qsar) models were employed for prediction the activity of p2x7 receptor antagonists. a data set consisted of 50 purine derivatives was utilized in the model construction where 40 and 10 of these compounds were in the training and test sets respectively. a suitable group of calculated molecular descriptors was selected by employing stepwise multiple ...

QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using statistical learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity. However, predictions from a QSAR model are difficult to assess if their prediction intervals are unknown...

      A series of angiotensin II (A II) receptor antagonist of some substituted 5-(biphenyl-4-ylmethyl) pyrazole were subjected to QSAR analysis using Hansch and Fujita-Ban model, by using combination of thermodynamic, electronic, spatial descriptor and presence or absence of substituent respectively. Several QSAR model were obtained using stepwise regression analysis. Two models from both the ...

Objective: Thiazolidine-2,4-dione (TZD) are the well known anti-diabetic scaffold. Very recently, several TZD based anti-cancer agents have came into limelight for treating mutant cancer forms. In order to establish and understand the relationship of biological activity with that of physiochemical parameters associated with the structure, twodimensional (2D-QSAR), group-based (G-QSAR), and thre...

Quantitative structure-activity relationship (QSAR) models were employed for prediction the activity of P2X7 receptor antagonists. A data set consisted of 50 purine derivatives was utilized in the model construction where 40 and 10 of these compounds were in the training and test sets respectively. A suitable group of calculated molecular descriptors was selected by employing stepwise multiple ...