Structural-activity classifications

Zmuidinavicius D, Didziapetris R, Japertas P, Avdeef A and Petrauskas A. Classification structure-activity relations (C-SAR) in prediction of human intestinal absorption. J Pharm Sci 2003 92 621-33. 

The Danish EPA has developed an advisory list for self-classification of dangerous substances including 20 624 substances. The substances have been identified by means of QSAR models (Quantitative Structure-Activity Relationship) as having acute oral toxicity, sensitization, mutagenicity, carcinogenicity, and/or danger to the aquatic environment. 

More recently (2006) we performed and reported quantitative structure-activity relationship (QSAR) modeling of the same compounds based on their atomic linear indices, for finding fimctions that discriminate between the tyrosinase inhibitor compounds and inactive ones [50]. Discriminant models have been applied and globally good classifications of 93.51 and 92.46% were observed for nonstochastic and stochastic hnear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67 and 89.44% [50]. In addition to this, these fitted models have also been employed in the screening of new cycloartane compounds isolated from herbal plants. Good behavior was observed between the theoretical and experimental results. These results provide a tool that can be used in the identification of new tyrosinase inhibitor compounds [50]. 

I 6 H-bonding Parameterization in Quantitative Structure-Activity Relationships i Drug Design Tab. 6.5 Biopharmaceutics classification for 254 Drugs on the basis of HYBOT descriptors. 

Measurement of dissociation equilibrium constants, which is of particular value in receptor classification and in the study of structure/activity relationships, where the effects of changes in chemical structure on affinity (and efficacy) are explored. 

Thus, cholinergic receptor classification can be considered in terms of three stages of development. Initially, Dale [2] distinguished nicotinic and muscarinic receptor subtypes with crude alkaloids. Then, chemical synthesis and structure-activity relationships clearly revealed that nicotinic and muscarinic receptors were heterogeneous, but chemical selectivity could not come close to uncovering the true diversity of receptor subtypes. Lastly, analysis of subtypes came from molecular cloning, making possible the classification of receptors on the basis of primary structure (Fig. 11-2). 

FIGURE 19-4 Schematic illustration of the structure and classification of mammalian RGS proteins. All the proteins contain a highly conserved RGS domain that has GAP activity. Most of the proteins contain additional domains that mediate other functions. The figure does not include several other types of homologous proteins, which lack the RGS domain but nevertheless are considered members of the RGS superfamily. 

Basak, S. C., Mills, D., Hawkins, D. M. Predicting allergic contact dermatitis A hierarchical structure-activity relationship (SAR) approach to chemical classification using topological and quantum chemical descriptors. J. Comput. Aided Mol. Des., 2008, 22, 339-343. 

The large number and diversity of available /3-lactams, mainly penicillins and cephalosporins, necessitate their classification. Penicillins can be classified primarily according to chemical structure. Table 5.2 shows that there is good correspondence between chemical structure and properties. The categorization of cephalosporins into chemically similar groups is not useful because their antimicrobial spectrum is not closely correlated with chemical structure, and classification into generations is based on their spectrum of microbial activity (Table 5.3). 

Marchini, S., Passerini, Hoglund, M.D., Pino, A., and Nendza, M. Toxicity of aryl- and benzylhalides to Daphnia magna and classification of their mode of action based on quantitative structure-activity relationship, Environ. Toxicol Chem., 18(12) 2759-2766, 1999. 

Yamashita, F., Hara, H., Ito, T., Hashida, M. Novel hierarchical classification and visualization method for multiobjective optimization of drug properties application to structure-activity relationship analysis of cytochrome P450 metabolism. J. Chem. Inf. Model. 2008, 48, 364-9. 

Key Words Biological activity cell-based partitioning chemical descriptors classification clustering distance-based design diversity selection high-throughput screening quantitative structure-activity relationship. 

In our study we compare two diversity-driven design methods (uniform cell coverage and clustering), two analysis methods motivated by similarity (cell-based analysis and cluster-classification), and two descriptor sets (BCUT and constitutional). Thus, our study addresses some of the many questions arising in a sequential screen how to choose the initial screen, how to analyze the structure-activity data, and what molecular descriptor set to use. The study is limited to one assay and thus cannot be definitive, but it at least provides preliminary insights and reveals some trends. 

Key Words Structure-activity relationship studies genetic algorithms pattern recognition olfaction musks classification molecular descriptors. 

Cos, P. et al., Structure-activity relationship and classification of flavonoids as inhibitors of xanthine oxidase and superoxide scavengers, J. Nat. Prod., 61, 71, 1998. 

Elucidation of the structural requirements for drug interaction at the recognition site is by the study of structure-activity relationships (SAR), in which, according to a specific biologic response, the effects of systematic molecular modification of a parent drug structure are determined. Such studies have permitted the classification of discrete classes of pharmacological receptors. 

Amino acids, zwitterions, cysteine Peptides, protein structures Enzyme classification Enzyme activity Third hour exam Chemical messengers... 

In the hazard identification process for chemicals that cause stochastic effects described above (EPA, 1987a), the weight-of-evidence classification is determined primarily by observations of tumors in animals or humans. Other information about the properties of a chemical, structure-activity relationships for other chemicals that cause stochastic effects, and the influence of a chemical on the carcinogenic process often is limited and plays only a modulating role in the weight-of-evidence classification based on tumor findings. 

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). 

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