A new topological method that makes it possible to predict the properties of molecules on the basis of their chemical structures is applied in the present study to quinolone antimicrobial agents. This method uses neural networks in which training algorithms are used as well as different concepts and methods of artificial intelligence with a suitable set of topological descriptors. This makes it possible to determine the minimal inhibitory concentration (MIC) of quinolones. Analysis of the results shows that the experimental and calculated values are highly similar. It is possible to obtain a QSAR interpretation of the information contained in the network after the training has been carried out.

The molecular connectivity method has been applied to the study of pharmacological properties, among which are found the angor treatment dose, alpha-distribution half-life and intravenous LD50 in mouse, of a group of beta-blocker agents, verifying its application in the prediction of theoretic values for said pharmacological properties. To do this, the obtained multiple regression functions of the corresponding connectivity indices were used in relation with the experimental values of the properties, which are accompanied by the statistical parameters used in their selection criteria, as well as the corresponding random and cross-validation studies of said functions, which corroborate the good correlation of the selected equations.

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