Quality quantitative structure activity

The concept of the similarity of molecules has important ramifications for physical, chemical, and biological systems. Grunwald (7) has recently pointed out the constraints of molecular similarity on linear free energy relations and observed that Their accuracy depends upon the quality of the molecular similarity. The use of quantitative structure-activity relationships (2-6) is based on the assumption that similar molecules have similar properties. Herein we present a general and rigorous definition of molecular structural similarity. Previous research in this field has usually been concerned with sequence comparisons of macromolecules, primarily proteins and nucleic acids (7-9). In addition, there have appeared a number of ad hoc definitions of molecular similarity (10-15), many of which are subsumed in the present work. Difficulties associated with attempting to obtain precise numerical indices for qualitative molecular structural concepts have already been extensively discussed in the literature and will not be reviewed here. 

Trieff NM, Ramanujam VMS, Stara IF, et al. 1991. Water quality criteria assessment for chlorinated benzenes using the quantitative structure activity relation approach and porphyrogenic endpoint in rats. Int J Environ Health Res 1 215-230. 

Odor and taste quality can be mapped by multidimensional scaling (MDS) techniques. Physicochemical parameters can be related to these maps by a variety of mathematical methods including multiple regression, canonical correlation, and partial least squares. These approaches to studying QSAR (quantitative structure-activity relationships) in the chemical senses, along with procedures developed by the pharmaceutical industry, may ultimately be useful in designing flavor compounds by computer. 

It is not yet possible to design a molecule with specific odor (or taste) characteristics because the relations between sensory properties of flavor compounds and their molecular properties are not well understood. As a consequence, the development of compounds with desired flavor qualities has had to rely on relatively tedious synthetic approaches. Recent advances, however, in computer-based methods developed by the pharmaceutical industry to study QSAR (quantitative structure-activity relationships) may ultimately be helpful in the rational design of new flavor-structures with predictable sensory attributes. Results from QSAR studies may also provide insight into the mechanism of the molecule-receptor interaction. 

As the uses of toxicological-based quantitative structure-activity relationships (QSARs) move into the arenas of priority setting, risk assessment, and chemical classification and labeling the demands for a better understanding of the foundations of these QSARs are increasing. Specifically, issues of quality, transparency, domain identification, and validation have been recognized as topics of particular interest (Schultz and Cronin, 2003). 

A9.5.2.4.1 For organic substances experimentally derived high-quality Kow values, or values which are evaluated in reviews and assigned as the recommended values , are preferred over other determinations of Kow. When no experimental data of high quality are available, validated Quantitative Structure Activity Relationships (QSARs) for log Kow may be used in the classification process. Such validated QSARs may be used without modification to the agreed criteria if they are restricted to chemicals for which their applicability is well characterized. For substances like strong acids and bases, substances which react with the eluent, or surface-active substances, a QSAR estimated value of Kow or an estimate based on individual -octanol and water solubilities should be provided instead of an analytical determination of Kow (EEC A.8., 1992 OECD 117, 1989). Measurements should be taken on ionizable substances in their non-ionized form (free acid or free base) only by using an appropriate buffer with pH below pK for free acid or above the pK for free base. 

The ubiquitous involvement of these materials in chirality induction for the purposes of assaying small molecules, determining enantiomeric purities, quality control, and quantitative structure-activity relationships (QSAR) is reviewed later in this article. The detector that is common to all these applications is CD. 

In addition to the biochemistry introduced in this chapter, a great deal of emphasis is placed on the determination of the activity of a compound by an analysis of its structure. Quantitative structure-activity relationships (QSAR), used judiciously, have the ability to help set testing priorities and identify potentially toxic materials in mixtures. Heavily reliant upon the quality of the toxicity data discussed in Chapter 4, these methods use sophisticated statistical techniques or analysis of interaction of a toxicant with the receptor to estimate toxicity. A method that uses structure-activity relationships coupled with availability and an assumed additive model for toxicity is presented to estimate the risk due to polyaromatic hydrocarbons (PAHs). 

Yu SJ, Keenan SM, Tong W, Welsh WJ. Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative structure-activity relationship (3D-QSAR) models predicting the estrogenic activity of xenoestrogens. Chem Res Toxicol 2002 15 1229-34. 

Quantitative structure-activity relationships have been used since the 1960s to model receptor and enzyme affinity, as well as physicochemical properties. The renewed interest in QSAR [25] arises from the recognition that an early prediction of AD M ET properties ensures compound quality and avoids early development failure related to... 

Three major approaches to the prediction of aqueous solubility of organic chemicals using Quantitative Structure Activity Relationship (QSAR) techniques arc reviewed. The rationale behind six QSAR models derived from these three approaches, and the quality of their fit to the experimental data are summarized. Their utility and predictive ability are examined and compared on a common basis. Three of the models employed octanol-water partition coefficient as the primary descriptor, while two others used the solvatochromic parameters. The sixth model utilized a combination of connectivity indexes and a modified polarizability parameter. Considering the case of usage, predictive ability, and the range of applicability, the model derived from the connectivity- polarizability approach appears to have greater utility value. 

---