Traditional methods of assessing chemical toxicity of various compounds require tests on animals, which raises ethical concerns and is expensive. Current legislation may lead to a further increase of demand for laboratory animals in the next years. As a result, automatically generated predictions using Quantitative Structure–Activity Relationship (QSAR) modelling approaches appear as an attract...

Drug design is an iterative process which begins with a compound that displays an interesting biological profile and ends with optimizing both the activity profile for the molecule and its chemical synthesis. The process is initiated when the chemist conceives a hypothesis which relates the chemical features of the molecule (or series of molecules) to the biological activity. Without a detailed...

Quantitative structure-activity relationship (QSAR) correlations have been widely applied for biological activities over several decades.1-7 Also, many applications of quantitative structure-property relationships (QSPR) are known in analytical chemistry.3,8-14 For instance, we recently successfully used15 our CODESSA (ComprehensiVe Descriptors for Structural and Statistical Analysis) QSPR prog...

The health effects of perfluorooctanoic acid (PFOA) on humans remain controversial because of contradictory experimental and epidemiological studies. In this study, we used three-dimensional quantitative structure-activity relationship (3D-QSAR) method by applying Surflex-dock to study the binding modes between PFOA and human estrogen receptor (hERα), human androgen receptor (hAR) and human thy...

Multiple linear regression analysis was performed on the quantitative structureactivity relationships (QSAR) of the triazoloquinazoline adenosine antagonists for human A3 receptors. The data set used for the QSAR analysis encompassed the activities of 33 triazoloquinazoline derivatives and 72 physicochemical descriptors. A template molecule was derived using the known molecular structure for on...

Current practice in Quantitative Structure Activity Relationship (QSAR) methods usually involves generating a great number of chemical descriptors and then cutting them back with variable selection techniques. Variable selection is an effective method to reduce the dimensionality but may discard some valuable information. This paper introduces Locally Linear Embedding (LLE), a local non-linear ...

Quantitative structure-activity relationship (QSAR) is a branch of computer aided drug discovery that relates chemical structures to biological activity. Two well established and related QSAR descriptors are two- and three-dimensional autocorrelation (2DA and 3DA). These descriptors encode the relative position of atoms or atom properties by calculating the separation between atom pairs in term...

The quantitative structure activity relationship paradigm provides an excellent avenue for investigating ligand–receptor interactions in medicinal chemistry/toxicology. Lateral validation of models formulated using this approach allows for a cohesive understanding of the mechanistic underpinnings of a specific class of related molecules. The diverse biological activities of substituted phenols ...

BACKGROUND The topological maximum cross correlation (TMACC) descriptors are alignment-independent 2D descriptors for the derivation of QSARs. TMACC descriptors are generated using atomic properties determined by molecular topology. Previous validation (J Chem Inf Model 2007, 47: 626-634) of the TMACC descriptor suggests it is competitive with the current state of the art. RESULTS Here, we il...

A quantitative structure activity relationship (QSAR) study on tetrasubstituted pyrazoles as high affinity ligands for the estrogen receptor (both ERα and ERβ subtypes) have been performed using various combinations of hydrophobic (MlogP), steric (MR) and electronic (Xeq) descriptors. The regression analysis of the data has shown better results in multiparametric regressions upon introduction o...