0000000000591768
AUTHOR
Ma. Teresa Salabert‐salvador
showing 3 related works from this author
Artificial Neural Networks and Linear Discriminant Analysis: A Valuable Combination in the Selection of New Antibacterial Compounds
2004
A set of topological descriptors has been used to discriminate between antibacterial and nonantibacterial drugs. Topological descriptors are simple integers calculated from the molecular structure represented in SMILES format. The methods used for antibacterial activity discrimination were linear discriminant analysis (LDA) and artificial neural networks of a multilayer perceptron (MLP) type. The following plot frequency distribution diagrams were used: a function of the number of drugs within a value interval of the discriminant function and the output value of the neural network versus these values. Pharmacological distribution diagrams (PDD) were used as a visualizing technique for the i…
QSAR Analysis of Hypoglycemic Agents Using the Topological Indices
2001
The molecular topology model and discriminant analysis have been applied to the prediction of some pharmacological properties of hypoglycemic drugs using multiple regression equations with their statistical parameters. Regression analysis showed that the molecular topology model predicts these properties. The corresponding stability (cross-validation) studies performed on the selected prediction models confirmed the goodness of the fits. The method used for hypoglycemic activity selection was a linear discriminant analysis (LDA). We make use of the pharmacological distribution diagrams (PDDs) as a visualizing technique for the identification and selection of new hypoglycemic agents, and we …
Drugs and Nondrugs: An Effective Discrimination with Topological Methods and Artificial Neural Networks
2003
A set of topological and structural descriptors has been used to discriminate general pharmacological activity. To that end, we selected a group of molecules with proven pharmacological activity including different therapeutic categories, and another molecule group without any activity. As a method for pharmacological activity discrimination, an artificial neural network was used, dividing molecules into active and inactive, to train the network and externally validate it. The following plot frequency distribution diagrams were used: a function of the number of drugs within a value interval, and the output value of the neural network versus these values. Pharmacological distribution diagram…