In this paper the Multi-linear regression analysis has been applied for QSAR study. The relationship has been worked out between the Log 1/C values of a series of compounds and certain quantum chemical and energy descriptors. The QSAR studies of Triazines inhibiting dihydrofolate reductase based on quantum chemical and energy descriptors shows that among all the 28 QSAR models PA51 to PA 78, th...

The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR...

Insubria QSAR PaDEL-Descriptor model for prediction of Endocrine Disruptors Chemicals (EDC) Estrogen Receptor (ER)-binding affinity. 1.2.Other related models: J.Li and P.Gramatica. The importance of molecular structures, endpoints’ values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders, Mol. Divers. 14, 2010, pp 687-696. [8] 1.3.Software cod...

A general feeling of disillusionment with QSAR has settled across the modeling community in recent years. Most practitioners seem to agree that QSAR has not fulfilled the expectations set for its ability to predict biological activity. Among the possible reasons that have been proposed recently for this disappointment are chance correlation, rough response surfaces, incorrect functional forms, ...

Two dimensional quantitative structure activity relationship (QSAR) study on series of substituted 2-azetidinone derivatives was performed by using V-LIFE MDS 3.0 software. Several statistical expression for 2D QSAR were developed using statistical methods like multiple regeression, principle component regression, partial least square regression etc. Out of several models, the best five 2D QSAR...

Quantitative structure activity relationships (QSARs) are functions that predict bioactivity from compound structure. Although almost every form of statistical and machine learning method has been applied to learning QSARs, there is no single best way of learning QSARs. Therefore, currently the QSAR scientist has little to guide her/him on which QSAR approach to choose for a specific problem. T...

QSAR models of 22 benzamidine derivatives reported as inhibitors of thrombin have been developed using the descriptors heat of formation, valence connectivity index, shape index, solvent accessibility surface area, molar refractivity, log P and molecular weight. QSAR models, in which either heat of formation or shape index or molar refractivity is present, have good predictive powers as correla...

The data have been obtained from the Heme Oxygenase Database (HemeOxDB) and refined according to the 2D-QSAR requirements. These data provide information about a set of more than 380 Heme Oxygenase-1 (HO-1) inhibitors. The development of the 2D-QSAR model has been undertaken with the use of CORAL software using SMILES, molecular graphs and hybrid descriptors (SMILES and graph together). The 2D-...

Antioxidants are important defenders of the human body against nocive free radicals, which are the causative agents of most life-threatening diseases. The immense biomedicinal utility of antioxidants necessitates the development and design of new synthetic antioxidant molecules. The present report deals with the modeling of a series of chromone derivatives, which was done to provide detailed in...

QSAR models for four skin penetration enhancer data sets of 61, 44, 42, and 17 compounds were constructed using classic QSAR descriptors and 4D-fingerprints. Three data sets involved skin penetration enhancement of hydrocortisone and hydrocortisone acetate. The other data set involved skin penetration enhancement of fluorouracil. The measure of penetration enhancement is the ratio of the net pe...