# QSAR Quantitative Structure and

Apart from finding structures that give energy minima, most molecular mechanics packages will calculate structural features such as the surface area or the molecular volume. Quantities such as these are often used to investigate relationships between molecular structure and pharmacological activity. This field of human endeavour is called QSAR (quantitative structure and activity relations). [c.56]

Besides the aforementioned descriptors, grid-based methods are frequently used in the field of QSAR quantitative structure-activity relationships) [50]. A molecule is placed in a box and for an orthogonal grid of points the interaction energy values between this molecule and another small molecule, such as water, are calculated. The grid map thus obtained characterizes the molecular shape, charge distribution, and hydrophobicity. [c.428]

The QSAR (quantitative structure-activity relationship) approach has been considered for the identification of toxicants that bind to steroid and aryl [c.50]

All the techniques described above can be used to calculate molecular structures and energies. Which other properties are important for chemoinformatics Most applications have used semi-empirical theory to calculate properties or descriptors, but ab-initio and DFT are equally applicable. In the following, we describe some typical properties and descriptors that have been used in quantitative structure-activity (QSAR) and structure-property (QSPR) relationships. [c.390]

A wide field of applications for chemical data mining is drug design. In short, drug design starts with a compound which has an interesting biological profile and optimizes the compound as well as its activity (see Section 10.4). Thus, the information about the biological activity of a compound is a crucial aspect in drug design. The relationship between a structure and its biological activity is represented by so-called quantitative structure-activity relationships (QSAR) (see Section 10.4). The field of QSAR can be approached via chemical data mining Starting from the structure input, e.g., in the form of a connection table (see Section 2.5), a 2D or 3D model of the structure is calculated. Ensuing secondary information, e.g., in the form of physicochemical properties such as charges, is generated for these structures. The enhanced structure model is then the basis for calculating a descriptor, i.e., a structure code in the form of a vector to which computational methods, for example statistical methods or neural networks, can be applied. These methods can then fulfill various data mining tasks such as classification or establishment of QSAR models which can finally be employed for the prediction of properties such as biological activities. [c.474]

Quantitative structure property relationships (QSPR) and, when applied to biological activity, quantitative structure activity relationships (QSAR) are methods for determining properties due to very sophisticated mechanisms purely by a curve ht of that property to aspects of the molecular structure. This allows a property to be predicted independent of having a complete knowledge of its origin. For example, drug activity can be predicted without knowing the nature of the binding site for that drug. QSPR is covered in more detail in Chapter 30. [c.108]

Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology W. Karcher, J. Devillers, Eds., Kluwer, Dordrecht (1990). [c.251]

Quantitative Structure—Activity Relationships (QSAR). Quantitative Stmcture—Activity Relationships (QSAR) is the name given to a broad spectmm of modeling methods which attempt to relate the biological activities of molecules to specific stmctural features, and do so in a quantitative manner (see Enzyme INHIBITORS). The method has been extensively appHed. The concepts involved in QSAR studies and a brief overview of the methodology and appHcations are given here. [c.168]

Quantitative Structure—Activity Relationship Design. Increasing economic pressures toward more, better, and cheaper pesticides have led to the development and appHcation of the Quantitative Stmcture—Activity Relationship (QSAR) paradigm and related experimental design principles for pesticides (4). Theoretically, quantitative deterrnination of the relationships between chemical stmcture and biological and environmental properties of a molecule should permit the design of a novel molecule with exacdy those properties considered ideal for the intended appHcation. In 1964, using earlier studies of the relationships of chemical composition to biological activity (5), the use of multivariate linear regression analyses to develop the quantitative stmcture—activity rules requited for successful QSAR appHcation was outlined (6). Subsequently, QSAR has been refined, expanded, and successfully appHed in the chemorational design of pesticidal compounds (3,7—11). [c.39]

Quantitative Structure—Activity Relationships. Many quantitative stmcture—activity relationship (QSAR) studies of progestins have appeared in the Hterature and an extensive review of this work is available (174). QSAR studies attempt to correlate electronic, steric, and/or hydrophobic properties to progestational activity or receptor binding affinity. A review focusing on the problems associated with QSAR of steroids has been pubUshed (175). [c.220]

Many different approaches to QSAR have been developed since Hansch s seminal work. These include both two-dimensional (2D) and 3D QSAR methods. The major differences among these methods can be analyzed from two viewpoints (1) the strucmral parameters that are used to characterize molecular identities and (2) the mathematical procedure that is employed to obtain the quantitative relationship between a biological activity and the structural parameters. [c.359]

PW91 (Perdew, Wang 1991) a gradient corrected DFT method QCI (quadratic conhguration interaction) a correlated ah initio method QMC (quantum Monte Carlo) an explicitly correlated ah initio method QM/MM a technique in which orbital-based calculations and molecular mechanics calculations are combined into one calculation QSAR (quantitative structure-activity relationship) a technique for computing chemical properties, particularly as applied to biological activity QSPR (quantitative structure-property relationship) a technique for computing chemical properties [c.367]

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. [c.432]

The fundamental assumption of SAR and QSAR (Structure-Activity Relationships and Quantitative Structure-Activity Relationships) is that the activity of a compound is related to its structural and/or physicochemical properties. In a classic article Corwin Hansch formulated Eq. (15) as a linear frcc-cncrgy related model for the biological activity (e.g.. toxicity) of a group of congeneric chemicals [37, in which the inverse of C, the concentration effect of the toxicant, is related to a hy-drophobidty term, FI, an electronic term, a (the Hammett substituent constant). Stcric terms can be added to this equation (typically Taft s steric parameter, E,). [c.505]

A quantitative structure-activity relationship (QSAR) relates numerical properties of tl molecular structure to its activity by a mathematical model. The term quantitative stru ture-property relationship (QSPR) is also used, particularly when some property oth( than biological activity is concerned. In drug design, QSAR methods have often bee used to consider qualities beyond in vitro potency. The most potent enzyme inhibitor is ( little use as a drug if it cannot reach its target. The in vivo activity of a molecule is often composite of many factors. A structure-activity study can help to decide which featurt of a molecule give rise to its overall activity and help to make modihed compounds wil enhanced properties. The relationship between these numerical properties and the activil is often described by an equation of the general form [c.711]

Quantitative Structure-Activity Relationship (QSAR) Methods. These methods (62) are computer algorithms that constmct mathematical functions correlating the chemical stmcture of a compound to its biological inhibitory activity. In many cases, this has led to the prediction of a compound s biological activity after obtaining data from a series of stmcturaHy similar molecules (63,64). For one QSAR approach, the Hansch method (65—67), it is assumed that the relative potency of an analogue is related to an additive combination of terms related to its physicochemical parameters such as piQ partition coefficient, and molecular refractivity. The values of the physicochemical properties and the values of concentration necessary for eliciting a standard biological effect ate measured for a series of stmcturaHy related compounds. Next, a set of many equations with many unknowns is obtained and solved for by a least-squares multiple regression analysis. In another approach called the Free-Wilson method (68), it is assumed that the relative potency of an analogue is related to an additive combination of terms described by variables that indicate the presence or absence of functional groups. For systems that cannot be accurately described by either of these two methods alone, the methods can be combined. With hybrid Hansch—Free-Wilson equations, the effects of functional groups on biological activity that cannot be fully accounted for by the physicochemical properties alone can be quantified. Once the mathematical function has been deterrnined by one of these methods, the variables for a new but related compound can be plugged into the equation, and the equations solved for its anticipated biological potency. [c.327]

The quantitative structure-activity relationship (QSAR) approach was first introduced by Hansch and coworkers [23,24] on the basis of implications from linear free energy relationships (LFERs) in general and the Hammett equation in particular [25]. It is based upon the assumption that the difference in physicochemical properties accounts for the differences in biological activities of compounds. According to this approach, the changes in physicochemical properties that affect the biological activities of a set of congeners are of three major types electronic, steric, and hydrophobic [26]. These structural properties are often described by Hammett electronic constants [27], Verloop STERIMOL parameters [28], hydrophobic constants [27], etc. The quantitative relationships between biological activity (or chemical property) and the structural parameters could be conventionally obtained by using multiple linear regression (MLR) analysis. [c.358]

In 3D searching strategies, the relevant molecules will be found by searching the fitting 3-D molecular properties around the chirality center instead of similar functional groups, as it can be searched using 2D query structures. The facilities of ISIS/3D fulfil this ability to import and search 3D structures in CHIRBASE. The 3D structures were built with the program CORINA developed by Gasteiger s group [16] and included in TSAR [17], a fully integrated quantitative structure-activity relationship (QSAR) package. For each 2D structure, only a single conformation was stored in CHIRBASE because ISIS/3D allows conformationahy flexible substructure (CFS) searching [18]. In a CFS search, the conformational fitting is the process of rotating single bonds in 3D structures to fit the constraints of the query. Actually, searching a database of flexible conformations is quite efficient and does not require the storage of accurate models as produced from X-ray crystallographic data, as long as we always build the database with models derived from the same force field calculations and therefore control the errors. [c.106]

But why are 3D structures needed. Part of the answer to this question has already been given. As mentioned previously, a large variety of physical, chemical, and biological properties of a molecule are strongly dependent on its 3D structure. Therefore, studies which try to correlate chemical structures with a certain property under consideration - so-called QSAR/QSPl studies (Quantitative/Quahtative Structure-Activity/Property Relationship) - may gain more insight into the problem under investigation if 3D structural information is used. Modeling and prediction of biological activity, virtual screening and docking experiments (prediction of receptor/ligand interactions and complexes in biological systems), or investigations to model the chemical reactivity of a compound clearly require information on the 3D structure of the molecules imder consideration. In addition, the results of structure elucidation techniques which are based on experimental data, such as those obtained from X-ray crystallography, NMR or IR spectra, depend heavily on the quality of the initial geometries of the molecules during the structure refinement procedure. Furthermore, quantum mechanical or molecular mechanical calculations need at least a crude 3D molecular model as starting geometry. [c.96]

Chemoinformatics (or cheminformatics) deals with the storage, retrieval, and analysis of chemical and biological data. Specifically, it involves the development and application of software systems for the management of combinatorial chemical projects, rational design of chemical libraries, and analysis of the obtained chemical and biological data. The major research topics of chemoinformatics involve QSAR and diversity analysis. The researchers should address several important issues. First, chemical structures should be characterized by calculable molecular descriptors that provide quantitative representation of chemical structures. Second, special measures should be developed on the basis of these descriptors in order to quantify structural similarities between pairs of molecules. Finally, adequate computational methods should be established for the efficient sampling of the huge combinatorial structural space of chemical libraries. [c.363]

See pages that mention the term

**QSAR Quantitative Structure and**:

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Modelling molecular structures (2000) -- [ c.0 ]