Quantitative structure-activity relationship chemical reactivity

As the chemical models mentioned here refer to some fundamental thermochemical and electronic effects of molecules, their application is not restricted to the prediction of chemical reactivity data. In fact, in the development of the models extensive comparisons were made with physical data, and thus such data can also be predicted from our models. Furthermore, some of the mechanisms responsible for binding substrates to receptors are naturally enough founded on quite similar electronic effects to those responsible for chemical reactivity. This suggest the use of the models developed here to calculate parameters for quantitative structure-activity relationships (QSAR). 

There are several properties of a chemical that are related to exposure potential or overall reactivity for which structure-based predictive models are available. The relevant properties discussed here are bioaccumulation, oral, dermal, and inhalation bioavailability and reactivity. These prediction methods are based on a combination of in vitro assays and quantitative structure-activity relationships (QSARs) [3]. QSARs are simple, usually linear, mathematical models that use chemical structure descriptors to predict first-order physicochemical properties, such as water solubility. Other, similar models can then be constructed that use the first-order physicochemical properties to predict more complex properties, including those of interest here. Chemical descriptors are properties that can be calculated directly from a chemical structure graph and can include abstract quantities, such as connectivity indices, or more intuitive properties, such as dipole moment or total surface area. QSAR models are parameterized using training data from sets of chemicals for which both structure and chemical properties are known, and are validated against other (independent) sets of chemicals. 

Quantitative structure-activity relationships (QSARs) are important for predicting the oxidation potential of chemicals in Fenton s reaction system. To describe reactivity and physicochemical properties of the chemicals, five different molecular descriptors were applied. The dipole moment represents the polarity of a molecule and its effect on the reaction rates HOMo and LUMO approximate the ionization potential and electron affinities, respectively and the log P coefficient correlates the hydrophobicity, which can be an important factor relative to reactivity of substrates in aqueous media. Finally, the effect of the substituents on the reaction rates could be correlated with Hammett constants by Hammett s equation. 

Quantitative structure-activity relationship (QSAR) dates back to the nineteenth century and is a computer-based tool that attempts to correlate variations in structural or molecular properties of compounds with their biological activities. These physicochemical descriptors, which include parameters to account for hydrophobicity, topology, electronic properties, and steric effects, are determined empirically or, more recently, by computational methods. The premise is that the structure of a chemical determines the physiochemical properties and reactivities that underlie its biological and toxicological properties. Being able to predict potential adverse effects not only aids in the designed development of new chemicals but also reduces the need for animal testing. It may ultimately or potentially lead to better... 

The interaction of the atoms and electrons within a specific molecule determines the impact of the compound at the molecular level. The contribution of the physical-chemical characteristics of a compound to the observed toxicity is called quantitative structure-activity relationship (QSAR). QSAR has the potential of enabling environmental toxicologists to predict the environmental consequences of toxicants using only structure as a guide. The response of a chemical to ultraviolet radiation and its reactivity with the abiotic constituents of the environment determine the fate of a compound. 

Quantitative Structure-Activity Relationships (QSARs) refer to scientific methods for correlating chemical structures quantitatively with biological activity or chemical reactivity. 

Additive (group contribution) methods have a long tradition of successful use in predicting the properties of both ordinary molecules and macromolecules (polymers). They have formed the backbone of the quantitative structure-activity relationships (QSAR) [1,2] used to predict the chemical reactivity and the biological activity of molecules in medicinal and agricultural chemistry. They have also been used extensively in many quantitative structure-property relationships (QSPR) developed for the physical and chemical properties of polymers. 

Quantum-chemical molecular descriptors have been actively used in the quantitative structure-activity relationship studies of biological activities [1,2,72]. In the following, examples of QSARs involving quantum-chemical descriptors and applied on the enzymatic reactivity, pharmacological activity, and toxicity of compounds are discussed. 

The study of structure-reactivity relationships by the organic chemist Hammett showed that there is often a quantitative relationship between the two-dimensional structure of organic molecules and their chemical reactivity. Specifically, he correlated the changes in chemical properties of a molecule that result from a small change in its chemical structure that is, the quantitative linear relationship between electron density at a certain part of a molecule and its tendency to undergo reactions of various types at that site. For example, there is a linear relationship between the effea of remote substituents on the equilibrium constant for the ionization of an acid with the effect of these substituents on the rate or equilibrium constant for many other types of chemical reaction. The relative value of Hammett substituent constants describes the similarity of molecules in terms of electronic properties. Taft expanded the method to include the steric hindrance of access of reagents to the reaction site by nearby substituents, a quantitation of three-dimensional similarity. In addition, Charton, Verloop, Austel, and others extended and refined these ideas. Finally, Hansch and Fujita showed that biological activity frequently is also quantitatively correlated with the hydrophobic character of the substituents. They coined the term QSAR, Quantitative Structure-Activity Relationships, for this type of analysis. 

Chemometrics Multivariate View on Chemical Problems Combined Quantum Mechanics and Molecular Mechanics Approaches to Chemical and Biochemical Reactivity Environmental Chemistry QSAR Infrared Data Correlations with Chemical Structure Quantitative Structure-Activity Relationships in Drug Design Quantitative Structure-Property Relationships (QSPR). 

The history of quantitative structure-activity relationships dates back to the last century, when Crum-Brown and Fraser in 1865 postulated that there ought to be a relationship between physiological activities <1> and chemical structures C. Later, Richet correlated toxicities with aqueous solubility. Around 1900, Meyer and Overton found linear relationships between the narcotic potencies of organic compounds and their partitioning behavior. In the mid-1930s, Hammett defined a reaction constant p to describe the reactivity of aromatic systems R, expressed by rate constants k (or equilibrium constants K) and a parameter o to describe the electronic properties of aromatic substituents X (1 equation 1) (see Linear Free Energy Relationships (LFER)) ... 

Chemical reactivity and biological activity can be related to molecular structure and physicochemical properties. QSAR models can be established among hydrophobic-lipophilic, electronic, and steric properties, between quantum-mechanics-related parameters and toxicity and between environmental fate parameters such as sorption and tendency for bioaccumulation. The main objective of a QSAR study is to develop quantitative relationships between given properties of a set of chemicals and their molecular descriptors. To develop a valid QSAR model, the following steps are essential ... 

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