Quantitative structure-activity relationships potentially applicable

Hopfinger, A.J. (1983). Theory and Application of Molecular Potential Energy Fields in Molecular Shape Analysis A Quantitative Structure-Activity Relationship Study of 2,4-Diamino-5-benzylpyrimidines as Dihydrofolate Reductase Inhibitors. J.Med.Chem.,26, 990-996. 

Kireev, D.B., Fetisov, V.I. and Zefirov, N.S. (1994). Approximate Molecular Electrostatic Potential Computations. Applications to Quantitative Structure-Activity Relationships. J.Mol. Struct. (Theochem), 110,143-150. 

Locuson CW, Wahlstrom JL. Three-dimensional quantitative structure-activity relationship analysis of cytochromes p450 Effect of incorporating higher-affinity ligands and potential new applications. Drug Metab Dispos 2005 33 873-8. 

If a cytotoxic chemical is capable of traversing the stratum corneum, it may cause toxicity to the skin as a function of its inherent potential to modify cellular function. Complex quantitative structure activity relationship (QSAR) models developed to assess general cytotoxicity may be applicable to define this inherent toxic potential. The clearest approach to assessing chemical-induced damage to skin is to assess what abnormalities occur when the specific anatomical structures discussed above are perturbed after exposure to topical compounds, since this will be the response modeled in a computational toxicology exercise. 

Hopfinger, A.J. (1983) Theory and application of molecular potential energy fields in molecular shape analysis a quantitative structure-activity relationship study of 2,4-diamino-5-benzylpyrimidines as dihydrofolate reductase inhibitors. J. Med. Chem., 26,... 

In many cases, at least for screening purposes and for preliminary comparisons of several compounds, approximate information on the intrinsic stability of a molecule, taken as an index of persistence potential that is independent of environmental variables, can be useful. In these cases the use of predictive approaches based on the molecular properties and structure (QSAR quantitative structure-activity relationships) could be very helpful in the absence of experimental information. Although the application of QSARs for the prediction of persistence has not yet been developed for screening as it has for other ecotoxicological aspects (e.g. prediction of toxic effects or bioaccumulation), in the last few years there has been some promising progress (Tremolada et al, 1991 Vasseur etal., 1993 Macalady and Schwarzenbach, 1993). 

Artificial Intelligence in Chemistry Chemical Engineering Expert Systems Chemometrics Multivariate View on Chemical Problems Electrostatic Potentials Chemical Applications Environmental Chemistry QSAR Experimental Data Evaluation and Quality Control Fuzzy Methods in Chemistry Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach Machine Learning Techniques in Chemistry NMR Data Correlation with Chemical Structure Protein Modeling Protein Structure Prediction in ID, 2D, and 3D Quality Control, Data Analysis Quantitative Structure-Activity Relationships in Drug Design Quantitative Structure-Property Relationships (QSPR) Shape Analysis Spectroscopic Databases Structure Determination by Computer-based Spectrum Interpretation. 

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. 

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