Quantitative structure-activity applications

All the techniques described above can be used to calculate molecular structures and energies. Which other properties are important for chemoinformatics Most applications have used semi-empirical theory to calculate properties or descriptors, but ab-initio and DFT are equally applicable. In the following, we describe some typical properties and descriptors that have been used in quantitative structure-activity (QSAR) and structure-property (QSPR) relationships. 

T A and H Kalayeh 1991. Applications of Neural Networks in Quantitative Structure-Activity ationships of Dihydrofolate Reductase Inhibitors, journal of Medicinal Chemistry 34 2824-2836. ik M and R C Glen 1992. Applications of Rule-induction in the Derivation of Quantitative icture-Activity Relationships. Journal of Computer-Aided Molecular Design 6 349-383. 

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. 

Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology W. Karcher, J. Devillers, Eds., Kluwer, Dordrecht (1990). 

SS So, M Karplus. Evolutionary optimization in quantitative structure-activity relationship An application of genetic neural networks. J Med Chem 39 1521-1530, 1996. 

TA Andrea, H Kalayeh. Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. J Med Chem 34 2824-2836, 1991. 

Rogers D, Hopfinger AJ. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. I Chem Inf Comput Sci 1994 34(4) 854-66. 

Maddalena DJ. Applications of artificial neural networks to quantitative structure-activity relationships. Expert Opin Ther Patents 1996 6 239-51. 

Netzeva TI, Worth AP, Aldenberg T, Benigni R, Cronin MTD, Gramatica P et al. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM workshop 52. ATLA 2005 33 152-73. 

Ekins S, Kim RB, Leake BE, Dantzig AH, Schuetz E, Lan LB, et al. Application of three dimensional quantitative structure-activity relationships of P-glycoprotein inhibitors and substrates. Mol Pharmacol 2002 61 974-981. 

We recently reported a structure-activity model for variations In target organs (12) and are currently examining the possible application of the quantitative structure-activity approach to the problem of specles-to-specles differences In susceptibility toward nltrosamlne carcinogenesis (19). These two topics will be discussed In the remainder of this presentation. 

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. 

Aleksic et al. [47] estimated the hydrophobicity of miconazole and other antimycotic drugs by a planar chromatographic method. The retention behavior of the drugs have been determined by TLC by using the binary mobile phases acetone-n-hexane, methanol toluene, and methyl ethyl ketone toluene containing different amounts of organic modifier. Hydrophobicity was established from the linear relationships between the solute RM values and the concentration of organic modifier. Calculated values of RMO and CO were considered for application in quantitative structure activity relationship studies of the antimycotics. 

Besides the applications of the electrophilicity index mentioned in the review article [40], following recent applications and developments have been observed, including relationship between basicity and nucleophilicity [64], 3D-quantitative structure activity analysis [65], Quantitative Structure-Toxicity Relationship (QSTR) [66], redox potential [67,68], Woodward-Hoffmann rules [69], Michael-type reactions [70], Sn2 reactions [71], multiphilic descriptions [72], etc. Molecular systems include silylenes [73], heterocyclohexanones [74], pyrido-di-indoles [65], bipyridine [75], aromatic and heterocyclic sulfonamides [76], substituted nitrenes and phosphi-nidenes [77], first-row transition metal ions [67], triruthenium ring core structures [78], benzhydryl derivatives [79], multivalent superatoms [80], nitrobenzodifuroxan [70], dialkylpyridinium ions [81], dioxins [82], arsenosugars and thioarsenicals [83], dynamic properties of clusters and nanostructures [84], porphyrin compounds [85-87], and so on. 

Baeten, A., Tafazoli, M., Kirsch-Volders, M., and Geerlings, P. 1999. Use of the HSAB principle in quantitative structure-activity relationships in toxicological research Application to the genotoxicity of chlorinated hydrocarbons. Int. J. Quantum Chem. 74 351-355. 

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... 

Mackay D, Peterson S (1990) In Karcher W, Devillers J (eds) Practical applications of quantitative structure-activity relationships (QSAR) in environmental chemistry and toxicology, Kluwer Academic Publishers, Dordrecht, Holland, p 433... 

As the chemical models mentioned here refer to some fundamental thermochemical and electronic effects of molecules, their application is not restricted to the prediction of chemical reactivity data. In fact, in the development of the models extensive comparisons were made with physical data, and thus such data can also be predicted from our models. Furthermore, some of the mechanisms responsible for binding substrates to receptors are naturally enough founded on quite similar electronic effects to those responsible for chemical reactivity. This suggest the use of the models developed here to calculate parameters for quantitative structure-activity relationships (QSAR). 

Braumann, T. Determination of hydrophobic parameters by reversed-phase liquid chromatography theory, experimental techniques, and application in studies on quantitative structure-activity relationships, J. Chromatogr., 373 191-225, 1986. 

Fujiwara, H., Da, Y-Z., Ito, K., Takagi, T., and Nishioka, Y. The energy aspect of oilAvater partition and its application to the analysis of quantitative structure-activity relationships. Aliphatic alcohols in the liposomeAvater partition system. Bull. Chem. Soc. Jpn., 64(12) 3707-3712, 1991. 

Viswanadhan, V.N., Ghose, A.K., Revankar, G.R., and Robins, R.K. Atomic physiochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics, / Chem. Inf. Comput. Scl, 29(3) 163-172, 1989. 

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