Quantitative similarity-activity relationship method

The antioxidant efficiency of phenolic acids, as determined by the accelerated autooxidation of methyl linoleate and scavenging of the free radical 2,2-diphenyl-1-picrylhydrazyl (141) ° methods, was found to be inversely proportional to the maximal detector response potential in the voltammetric determination of these compounds. No similar correlation was found for the flavonoids . A good correlation was found between the O—H bond dissociation energy of a phenolic compound and its effectiveness as antioxidant, expressed as the rate constant of free radical scavenging . The bond dissociation energy of the phenol O—H bond was estimated by a three-dimensional quantitative structme-activity relationship method incorporating electron densities computed using the Austin Method 1 (AMI) followed by correlation of the... 

In spite of such interesting examples, it still remains an open question how molecular similarity is defined with respect to immunological response, since it is also known that cross-reactivity exists in some instances between STLs and mono- as well as diterpenoids [74], It appears therefore a promising topic for future studies to investigate this issue in more detail (i.e. with large sets of compounds and applying quantitative structure-activity relationship methods) in order to elucidate the general rules by which specificity in ACD cross-sensitivity is determined. 

Despite the straightforward definition of CoMFA, there are a number of serious problems and possible pitfalls. Several CoMFA modifications have been described which solve or avoid some of these problems (Section 7). In addition, alternatives to CoMFA were developed, e.g., comparative molecular similarity indices analysis (CoMSIA) (Section 5) and other 3D quantitative similarity-activity relationship (QSiAR) methods. " ... 

QSAJi Methods for Fluid Solubility Prediction. Several group contribution methods for predicting Hquid solubiHties have been developed. These methods as weU as other similar methods are often called quantitative stmcture-activity relationships (QSARs). This field is experiencing rapid development. 

C-H and N-H bond dissociation energies (BDEs) of various five- and six-membered ring aromatic compounds (including 1,2,5-oxadiazole) were calculated using composite ab initio CBS-Q, G3, and G3B3 methods. It was found that all these composite ab initio methods provided very similar BDEs, despite the fact that different geometries and different procedures in the extrapolation to complete incorporation of electron correlation and complete basis set limit were used. A good quantitive structure-activity relationship (QSAR) model for the C-H BDEs of aromatic compounds... 

In this chapter, we will give a brief introduction to the basic concepts of chemoinformatics and their relevance to chemical library design. In Section 2, we will describe chemical representation, molecular data, and molecular data mining in computer we will introduce some of the chemoinformatics concepts such as molecular descriptors, chemical space, dimension reduction, similarity and diversity and we will review the most useful methods and applications of chemoinformatics, the quantitative structure-activity relationship (QSAR), the quantitative structure-property relationship (QSPR), multiobjective optimization, and virtual screening. In Section 3, we will outline some of the elements of library design and connect chemoinformatics tools, such as molecular similarity, molecular diversity, and multiple objective optimizations, with designing optimal libraries. Finally, we will put library design into perspective in Section 4. 

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. 

Methods to predict the hydrolysis rates of organic compounds for use in the environmental assessment of pollutants have not advanced significantly since the first edition of the Lyman Handbook (Lyman et al., 1982). Two approaches have been used extensively to obtain estimates of hydrolytic rate constants for use in environmental systems. The first and potentially more precise method is to apply quantitative structure/activity relationships (QSARs). To develop such predictive methods, one needs a set of rate constants for a series of compounds that have systematic variations in structure and a database of molecular descriptors related to the substituents on the reactant molecule. The second and more widely used method is to compare the target compound with an analogous compound or compounds containing similar functional groups and structure, to obtain a less quantitative estimate of the rate constant. 

Quantitative structure-activity relationships are primarily used for drug design. The underlying principle is that the shape and noncovalent interactions are the main contributors to the selectivity of the binding of substrates to an active center. Therefore, it must be possible to correlate structural properties of substrates with their activity. The assumptions on which QSAR methods are generally based are that all substrates bind to the same site, that structurally related compounds bind with a similar orientation and that dynamic effects can be ignored. 

Another objective of this chapter is to explain how LFER fits in with respect to linear solvation energy relationships (LSER), quantitative structure-activity relationships (QSAR), and quantitative structure-property relationships (QSPR). Often, these methods are operationally quite similar. Their connection is addressed in the Background section. 

The next step was made by Klebe et al. [50]. Two 3D-QSAR methods were applied to get three-dimensional quantitative structure-activity relationships using a training set of 72 inhibitors of the benzamidine type with respect to their binding affinities toward Factor Xa to yield statistically reliable models of good predictive power [51-54] the widely used CoMFA method (for steric and electrostatic properties) and the comparative molecular similarity index analysis (CoMSlA) method (for steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor properties). These methods allowed the consideration of various physicochemical properties, and the resulting contribution maps could be intuitively interpreted. 

So, S.-S. and Karplus, M. (1997a). Three-Dimensional Quantitative Structure-Activity Relationships from Molecular Similarity Matrices and Genetic Neural Networks. 1. Method and Validations. J.Med.Chem., 40,4347-4359. 

Quantitative structure activity relationships (QSAR) are a method of estimating the toxic properties of a compound using the physical and structural makeup of a compound. These properties and the knowledge that similar compounds typically have similar modes of action make QSAR a possibility. In many instances no toxicity data are available for a compound for a variety of reasons. Perhaps the most interesting one is in the evaluation of proposed compounds of which only small amounts or none at all are available. QSAR can be instrumental in selecting compounds with the desired properties but with low toxicity to the environment. 

In addition, traditional quantitative structure-activity relationship (QSAR) models were reported. Gozalbes et al. attempted to predict the blood-brain barrier permeabilities of four arylacetamides using linear discriminant analysis [65], while Medina-Franco et al. discriminated between active and inactive BCG compounds using two-dimensional (2D) and three-dimensional (3D) structural-similarity methods [66]. 

The determinants of blood-brain barrier penetration are similar to the determinants of membrane permeability. They include lipophilicity (log P), H-bonding capacity, ionization prohle, size, and flexibility. An example of a simple quantitative structure-activity relationship (QSAR) equation to calculate the ratio of the steady state concentration of the drug molecule in the brain and in the blood have been described" (for a comprehensive review of the in silico methods see" ) ... 

In the last decades methods have been developed to describe quantitative structure-activity relationships and quantitative structure-property relationships, which deal with the modeling of relationships between structural and chemical or biological properties. The similarity of two compounds concerning their biological activity is one of the central tasks in the development of pharmaceutical products. A typical application is the retrieval of structures with defined biological activity from a database. Biological activity is of special interest in the development of drugs. 

The notion of structural similarity, and the measures that have been devised to assess it, have become of major importance over the past decade. In fact, the whole field of quantitative structure-activity relationships (QSAR) has been a burgeoning one during this period. So it is hardly surprising that a large number of different methods of comparing molecules to estimate their relative similarities have emerged [46]. A systematic study of several measures of intermolecular structural similarity revealed [47] that only minor differences exist between the various measures, and that consequently all of them could be regraded as... 

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