Quantitative structure-activity relationship prediction

Malygin, V.V., Sokolov, V.B., Richardson, R.J., Makhaeva, G.F. (2003). Quantitative structure-activity relationships predict the delayed neurotoxicity potential of a series of O-alkyl-O-methylchloroformimino phenylphosphonates. J. Toxicol. Environ. Health Part A 66 611-25. 

Lipnick, R.L., Johnson, D.E., Gilford, J.M., Bickings, C.K. and Newsome, L.D. 1985. Comparison of fish toxicity screening data for 55 alcohols with the quantitative structure-activity relationship predictions of minimum toxicity for nonreactive nonelectrolyte organic compounds. Environ. Toxicol. Chem. 4 281-296. 

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. 

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR). 

Smith PA, Sorich MJ, McKinnon RA, Miners JO. Pharmacophore and quantitative structure-activity relationship modeling complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity. J Med Chem 2003 46 1617-26. 

Wang YW, Liu HX, Zhao CY, Liu HX, Cai ZW, Jiang GB. Quantitative structure-activity relationship models for prediction of the toxicity of polybrominated diphenyl ether congeners. Environ Sci Technol 2005 39 4961-6. 

Bartlett A, Dearden JC, Sibley PR. Quantitative structure-activity relationships in the prediction of penicillin immunotoxicity. Quant Struct-Act Relat 1995 14 258-63. 

Meylan, W.M. Howard, P.H. (2003) A Review of Quantitative Structure-Activity Relationship Methods for the Prediction of Atmospheric Qxidation of Qrganic Chemicals. Environmental Toxicology and Chemistry, 22(8), 1724—1732. 

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]. 

Petrauskas, A. A., Kolovanov, E. A. ACD approaches for phys-chem data prediction. In 13th Eur. Symp. on Quantitative Structure-Activity Relationships, Dtisseldorf 2000, abstr. book p. 4. 

In a study by Andersson et al. [30], the possibilities to use quantitative structure-activity relationship (QSAR) models to predict physical chemical and ecotoxico-logical properties of approximately 200 different plastic additives have been assessed. Physical chemical properties were predicted with the U.S. Environmental Protection Agency Estimation Program Interface (EPI) Suite, Version 3.20. Aquatic ecotoxicity data were calculated by QSAR models in the Toxicity Estimation Software Tool (T.E.S.T.), version 3.3, from U.S. Environmental Protection Agency, as described by Rahmberg et al. [31]. To evaluate the applicability of the QSAR-based characterization factors, they were compared to experiment-based characterization factors for the same substances taken from the USEtox organics database [32], This was done for 39 plastic additives for which experiment-based characterization factors were already available. 

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. 

Spsted, H. et al., Ranking of hair dye substances according to predicted sensitization potency quantitative structure-activity relationships, Contact Dermatitis, 51, 241, 2004. 

Rodford, R., et al., Quantitative structure-activity relationships for predicting skin and respiratory sensitization, Environ. Toxicol Chem., 22, 1855, 2003. 

Benigni, R., Andreoli, C., Conti, L., Tafani, P., Cotta-Ramusino, M., Carere, A., Crebelli, R. Quantitative structure-activity relationship models correctly predict the toxic and aneuploidizing properties of halogenated methanes in Aspergillus nidulans. Mutagenesis 1993, 8, 301-305. 

Octanol/water partition coefficients, Pow, which measure the relative solubilities of solutes in octanol and in water, are widely used as descriptors in quantitative structure-activity relationships (QSAR), for example in pharmacological and toxicological applications.49 Since experimental values of these are not always available, a number of procedures for predicting them have been proposed (see references in Brinck et al.).50... 

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