Property-activity relationships

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). 

QSAR-PC Biosoft, 22 Hills Road, Cambridge, CB2 1JP, U.K. 200. Programs for investigation of property-activity relationships by regression analysis. 

Hansch analysis tries to correlate biological activity with physico-chemical properties by linear and nonlinear regression analysis, finding property-activity relationship models. 

The proposed slash between the two terms structure and response denotes both and and or , thus also involving property-property relationships as well as - similarity/diversity correlations. Therefore, quantitative property-property relationships (QPPR), property-activity relationships (QPAR), activity-activity relationships (QAAR), and similarity/diversity correlations fall, in a broader sense, within SRC studies. 

Hirokawa S, Imasaka T, Imasaka T. Chlorine substitution pattern, molecular electronic properties, and the nature of the ligand-receptor interaction Quantitative property-activity relationships of polychlorinated dibenzofurans. Chem Res Toxicol 2005 18 232-8. 

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. 

Hansch analysis tries to correlate biological activity with physico-chemical properties by linear and nonlinear regression analysis, finding property-activity relationship models. A Craig plot is a plot of two substituent parameters (e.g., Hansch-Fujita n and Hammett a values). The simplest Hansch analysis is based on the Hansch linear model [Kubinyi, 1988b], defined... 

Jantschi, L. and Bolboaca, S. (2007) Results from the use of molecular descriptors family on structure-property/activity relationships. Int. J. Mol Scl, 8, 189-203. 

Variables commonly used in PARs and SARs are summarized in Table 4. The main processes of interest relative to the bioactivity of aquatic contaminants are bioaccumulation, biodegradation, and acute toxicity (LC50), but inhibition of key biological processes such as respiration rate and photosynthesis also are used in some PARs and SARs as measures of a compound s toxicity. The physicochemical properties listed in Table 4 reflect molecular structure, but they are not structural characteristics themselves. Relationships based on these properties thus should be called property-activity relationships (PARs), and the term (quantitative) structure-activity relationship, (Q)SAR should be restricted to relationships based on structural or topological parameters. However, the literature is not consistent in this terminology, and the line between structural characteristics and properties resulting from structure is not always clear. 

TABLE 7. Simple Property-Activity Relationships for Organic Compounds... 

There are related fields of activity such as the search for molecular similarities (Johnson and Maggiora 1990), and for quantitative structure and property/activity relationships, where the insights gained from molecular classifications based on periodicity may be helpful. 

Figure 2. Composition functions for quantitative struc-tute-activity relauonship (QSAR) and property- activity relationship (PAR).

The question can be raised Is it possible to design a set of descriptors that will be useful for diverse applications In other words, can we design a basis that will serve for the characterization of diverse structure-property/activity relationships ... 

Development of the Latest Tools for Building up Nano-QSAR Quantitative Features—Property/Activity Relationships (QFPRs/QFARs)... 

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. 

Formally, the quasi-SMILES can replace the traditional SMILES in the above mentioned optimization procedure. However, in this case the new description should be used and the models should be named quantitative features — property/activity relationships (QFPRs/QFARs), since these models will be based on features which can be distant from the molecular strucmre (e.g. size, concentration, time exposure, etc.) that is used in classic QSPR/QSAR methods. 

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... 

Though details of computational investigation of properties and activities of nanomaterials are still being developed the quantitative features — property/activity relationships (QFPRs/QFARs) methods discussed in this chapter provide eflicient tool to predict various characteristics of nanospecies. They are developed following a set of mles (i) All examined eclectic systems of data are translated into quasi-SMILES with further prediction of the above mentioned endpoints by the same approach (ii) budding up these models using the quasi-SMILES can be carried out by the same algorithm (software) which is used in... 

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

Fig. 2 Composition functions for structure-activity and property-activity relationships (C = chemicals, R = real numbers, D = structural descriptors, M = molecular properties) Reprinted with permission source from [15]...

It should be mentioned here that the Occam s razor rule has been mentioned from time to time by those who continue to object to the apparent explosion of topological indices, which has occurred in more recent years in QSAR, but the simplest of competing theories being preferred does not extend to replacing theories with descriptors. Therefore, the simplest of competing descriptors should not necessarily be preferred. What is important for molecular descriptors is not how similar they are and how much they may duplicate one another, but do they differ one from another in some details that are important (for the structure-property-activity relationship). 

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. 

Knowledge of the microstructure of chitosan samples is essential for understanding the structure-property-activity relationships of chitin and chitosan products [ 18,38-42]. To date, only the effect of weight-averaged molecular weight (MW) and degree results... 

Putz, M. V. (2013a). Spectral-diagonal approach of structure-property (activity) relationships SD-QSP(A)R. The general formalism, Int. J. Chem. Model. 5(2/3), 357-367. 

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