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Tetracyclines are an old class of molecules that constitute a broad-spectrumantibiotics. Since the first member of tetracycline family were isolated, theclinical importance of these compounds as therapeutic and prophylactic agentsagainst a wide range of infections has stimulated efforts to define their modeof action as inhibitors of bacterial reproduction. We used three SARmethodologies for the analysis of biological activity of a set of 104tetracycline compounds. Our calculation were carried out using thesemi-empirical Austin Method One (AM1) and Parametric Method 3 (PM3).Electronic Indices Methodology (EIM), Principal Component Analysis (PCA) andArtificial Neural Networks (ANN) were applied to the classification of 14 oldand 90 new proposed derivatives of tetracyclines. Our results make evident theimportance of EIM descriptors in pattern recognition and also show that the EIMcan be effectively used to predict the biological activity of Tetracyclines.

Structure-Activity Relationship Investigation of Some New Tetracyclines by Electronic Index Methodology