Quantitative structure-property/activity

Multivariate calibration has the aim to develop mathematical models (latent variables) for an optimal prediction of a property y from the variables xi,..., jcm. Most used method in chemometrics is partial least squares regression, PLS (Section 4.7). An important application is for instance the development of quantitative structure—property/activity relationships (QSPR/QSAR). 

Among many theoretical approaches, the quantitative structure-property/ activity relationships (QSPR/QSAR) methods in conjunction with experimental data pave the way to characterization of properties of new compounds. Properly calibrated such methods provide tools for the prediction of physicochemical parameters (QSPR) and/or biological activity (QS AR) for substances, which have not been yet examined in experiments (Wiener, 1947a, b, 1948a, b Randic and Basak, 1999, 2001 Randic and Pompe, 2001a, b Basak et al., 2001). 

One solution to this quagmire has been the use of calculated properties estimated from the molecular structure of chemicals instead of their experimental data. Molecular descriptors calculated using different variations of the chemical stmcture lead to the development of quantitative structure-property/activity relationship (QSPR/QSAR) models. 

Quantitative Structure—Property/Activity Relationships (QSPR/QSAR) is one of the valuable tools of theoretical chemistry. To some extent, QSPR/QSAR analyses can be classified as investigations solely rely on and devoted to chemistry. However, in recent years more common situation emerges when QSPR/QSAR analysis accumulates and uses ideas and approaches adopted from two or more natural science areas. Unfortunately, at present these methods are only scantily involved in the nano-chemistry, nano-biology, and nano-ecology. 

In the cases of organic, inorganic, and organometaflic compounds, as well as for various polymers the quantitative structure—property/activity relationships (QSPRs/QSARs) approaches represent efficient and available tools one can use in order to predict numerical data related to an endpoint of unknown substances. In order to accomplish it and develop QSPR/QSAR model such approaches require... 

QSPR-QSAR Quantitative structure-property/activity relationships SMILES Simplified molecular input line entry system A Adjacency matrix... 

The book discusses a number of important problems in chemistry that have not been fully understood or fully appreciated, such as the notion of aromaticity and conjugated circuits, the generalized Htickel 4n + 2 Rule, and the nature of quantitative structure-property-activity relationships (QSARs), which have resulted in only partially solved problems and approximated solutions that are inadequate. It also describes advantages of mathematical descriptors in QSAR, including their use in screening eombinatorial libraries to search for structures with high similarity to the target compounds. 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Rogers D and A J Hopfinger 1994. Application of Genetic Function Approximation to Quantitatir Structure-Activity Relationships and Quantitative Structure-Property Relationships. Journal Chemical Information and Computer Science 34 854-866. 

When the property being described is a physical property, such as the boiling point, this is referred to as a quantitative structure-property relationship (QSPR). When the property being described is a type of biological activity, such as drug activity, this is referred to as a quantitative structure-activity relationship (QSAR). Our discussion will first address QSPR. All the points covered in the QSPR section are also applicable to QSAR, which is discussed next. 

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... 

D Rogers, AJ Hopflnger. Application of genetic function approximation to quantitative strac-ture-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34(4) 854-866, 1994. 

The prediction of the properties of molecules from a knowledge of their structure (quantitative structure-property relationships [QSPRs] or quantitative structure-activity relationships [QSARs]). ANNs can be used to determine QSPRs or QSARs from experimental data and, hence, predict the properties of a molecule, such as its toxicity in humans, from its structure. 

Thiadiazole 1 and its derivatives were used as model compounds for the calculation of molecular parameters related to physical properties for their use in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies <1999EJM41, 2003IJB2583, 2005JMT27>. 

Because of the large number of chemicals of actual and potential concern, the difficulties and cost of experimental determinations, and scientific interest in elucidating the fundamental molecular determinants of physical-chemical properties, considerable effort has been devoted to generating quantitative structure-property relationships (QSPRs). This concept of structure-property relationships or structure-activity relationships (QSARs) is based on observations of linear free-energy relationships, and usually takes the form of a plot or regression of the property of interest as a function of an appropriate molecular descriptor which can be calculated using only a knowledge of molecular structure or a readily accessible molecular property. 

There is a continuing effort to extend the long-established concept of quantitative-structure-activity-relationships (QSARs) to quantitative-structure-property relationships (QSPRs) to compute all relevant environmental physical-chemical properties (such as aqueous solubility, vapor pressure, octanol-water partition coefficient, Henry s law constant, bioconcentration factor (BCF), sorption coefficient and environmental reaction rate constants from molecular structure). 

The overall importance of the medium on the reaction rates has been shown previously, but the nature and extent of solute-solvent interactions can alter tremendously various properties of the nucleophile the variations are usually satisfactorily correlated by some of the several quantitative structure-activity relationships (QSAR) that have been discussed37,38,51,96. The term quantitative structure-property relationship (QSPR) has been recently proposed for cases where a specific property, such as the basicity, is examined97. 

In many cases of practical interest, no theoretically based mathematical equations exist for the relationships between x and y we sometimes know but often only assume that relationships exist. Examples are for instance modeling of the boiling point or the toxicity of chemical compounds by variables derived from the chemical structure (molecular descriptors). Investigation of quantitative structure-property or structure-activity relationships (QSPR/QSAR) by this approach requires multivariate calibration methods. For such purely empirical models—often with many variables—the... 

The second modeling approach discussed in this section presents an overview of the fundamentals of quantitative structure-activity relationships (i.e., QSARs [102-130]) and quantitative structure-property relationships (i.e., QSPRs [131-139]). It will show how such an approach can be used in order to estimate and predict sorption/desorption coefficients of various organic pollutants in environmental systems. 

Rogers, D. Hopfingee, A.J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 1994, 34, 854-866. Kubinyi, H. Variable selection in QSAR studies. 1. An evolutionary algorithm. Quantum Struct.-Act. Relat. 1994, 13, 285-294. 

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