QSARs establish a quantitative relationship between chemical structures and their properties [1]. In theory, QSAR models can be used to predict the properties of chemical structures, provided their structural information is available. In the recent years, there had been a growing awareness about QSARs and their applications. This is quite evident from their use for regulatory purposes. A new Eu...

The objective of this work was to investigate the potential of the quantitative structure-activity relationships (QSAR) approach for predictive modulation of molecular interaction kinetics. A multivariate QSAR approach involving modifications in peptide sequence and buffer composition was recently used in an attempt to predict the kinetics of peptide-antibody interactions as measured by BIACORE...

frame 24.2 | november 2011 | 105-115 I think that the feeling of horror . . . does not correspond, as most people believe, to what is bad for us, to what jeopardizes our interests. On the contrary, if they horrify us, objects that otherwise would have no meaning take on the highest present value in our eyes. Erotic activity can be disgusting; it can also be noble, ethereal, excluding sexual con...

Cyclo-oxygenase 2 (COX2) inhibiting drugs were subjected to comparative quantitative structure activity relationship (QSAR) analysis with an attempt to derive and to understand the relationship between the biological activity and molecular descriptors by multiple regression analysis. The different drugs that inhibit cyclo-oxygenase 2 enzyme were compared instead of subjecting one drug and its d...

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...

Three dimensional quantitative structure activity relationship between diazabicyclo[4.2.0]octanes and nicotinic acetylcholine receptor (halpha4beta2 and halpha3beta4) agonists was studied using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). From 11 CoMFA and CoMSIA models, CoMSIA with steric and electrostatic fields gave the best pre...

Quantum mechanical (QM) methods are becoming popular in computational drug design and development mainly because high accuracy is required to estimate (relative) binding affinities. For low-to medium-throughput in silico screening, (e.g., scoring and prioritizing a series of inhibitors sharing the same molecular scaffold) efficient approximations have been developed in the past decade, like lin...

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...