QSAR property relationship

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. 

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... 

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. 

Accordingly, sorption has received a tremendous amount of attention and any method or modeling technique which can reliably predict the sorption of a solute will be of great importance to scientists, environmental engineers, and decision makers (references herein and in Chaps. 2 and 3). The present chapter is an attempt to introduce an advanced modeling approach which combines the physical and chemical properties of pollutants, quantitative structure-activity, and structure-property relationships (i. e., QSARs and QSPRs, respectively), and the multicomponent joint toxic effect in order to predict the sorption/desorp-tion coefficients, and to determine the bioavailable fraction and the action of various organic pollutants at the aqueous-solid phase interface. 

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. 

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. 

K. L. E. (2009) How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR and QSAR in Environ Res 20, 241-266. 

Dunnivant, F.M., Elzerman, A.W. (1988) Aqueous solubility and Henry s law constant data for PCB congeners for evaluation of quantitative structure-property relationships (QSARs). Chemosphere 17, 525-541. 

Stanton DT, Jurs PC (1990) Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies. Anal Chem 62 2323—2329. Tetko iy Kovalishyn Vy Livingstone DJ (2001) Volume learning algorithm artificial neural networks for 3D-QSAR studies. J Med Chem 44 2411-2420. 

Linear and non-linear correlations of structural parameters and strain energies with various molecular properties have been used for the design of new compounds with specific properties and for the interpretation of structures, spectra and stabilities 661. Quantitative structure-activity relationships (QSAR) have been used in drug design for over 30 years 2881 and extensions that include information on electronic features as a third dimension (the electron topological approach, ET) have been developed and tested 481 (see Section 2.3.5). Correlations that are used in the areas of electron transfer, ligand field properties, IR, NMR and EPR spectroscopy are discussed in various other Chapters. Here, we will concentrate on quantitative structure-property relationships (QSPR) that involve complex stabilities 124 289-2911. 

Quantitative-structure-property relationship. A quantitative relationship between a specified property and the structure of a compound. Very similar to QSAR but of more relevance in coordination chemistry, where properties rather than activities are usually referred to. 

Whereas hard filters can be considered to be knowledge-driven, soft filters are the result of a data-driven approach. A quantitative structure-activity or structure-property relationship (QSAR/QSPR) is established to predict a property from a set of molecular descriptors. Examples are the above-mentioned in-silico prediction tools for frequent hitters [27] and drug-likeness [41,42] additional models for ADM E properties are described below. 

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