Quantitative structure - property

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. 

Two approaches to quantify/fQ, i.e., to establish a quantitative relationship between the structural features of a compoimd and its properties, are described in this section quantitative structure-property relationships (QSPR) and linear free energy relationships (LFER) cf. Section 3.4.2.2). The LFER approach is important for historical reasons because it contributed the first attempt to predict the property of a compound from an analysis of its structure. LFERs can be established only for congeneric series of compounds, i.e., sets of compounds that share the same skeleton and only have variations in the substituents attached to this skeleton. As examples of a QSPR approach, currently available methods for the prediction of the octanol/water partition coefficient, log P, and of aqueous solubility, log S, of organic compoimds are described in Section 10.1.4 and Section 10.15, respectively. 

P. C. Juts, Quantitative structure-property relationships, in Encyclopedia of Computational Chemistry, Volume 4, P. v. R. Schleyer, N. L. Allinger, T. Qaik, J. Gasteiger, P. A. KoUman, H. F. Schaefer III and P.R. Schreiner (Eds.), John Wiley Sons, Chichester, 1998, pp. 2320-2330. 

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... 

Quantitative Structure-Property Relationships. A useful way to predict physical property data has become available, based only on a knowledge of molecular stmcture, that seems to work well for pyridine compounds. Such a prediction can be used to estimate real physical properties of pyridines without having to synthesize and purify the substance, and then measure the physical property. 

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. 

Applications of neural networks are becoming more diverse in chemistry [31-40]. Some typical applications include predicting chemical reactivity, acid strength in oxides, protein structure determination, quantitative structure property relationship (QSPR), fluid property relationships, classification of molecular spectra, group contribution, spectroscopy analysis, etc. The results reported in these areas are very encouraging and are demonstrative of the wide spectrum of applications and interest in this area. 

Zheng W, Tropsha A. Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. J Chem Inf Comput Sci 2000 40(l) 185-94. 

Deardon, J.C. (2003) Quantitative Structure-Property Relationships for Prediction of Boiling Point, Vapor Pressure and Melting Point. Environmental Toxicology and Chemistry, 22(8), 1696-1709. 

Liu, R., So, S.-S. Development of quantitative structure-property relationship models for early ADME evaluation in drug discovery. 1. Aqueous solubility./. Chem. Inf. Comp. Set. 2001, 41, 1633-1639. 

S. Prediction of aqueous solubility of organic compounds using a quantitative structure-property relationship. J. Pharm. Sd. 2002, 91,1838-1852. 

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. 

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