0000000000591767

AUTHOR

Maria Jose Castro-bleda

showing 2 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…

Artificial neural networkChemistrybusiness.industryComputer Science::Neural and Evolutionary ComputationDiscriminant AnalysisPattern recognitionGeneral MedicineMicrobial Sensitivity TestsGeneral ChemistryFunction (mathematics)Interval (mathematics)Linear discriminant analysisPlot (graphics)Anti-Bacterial AgentsQuantitative Biology::Cell BehaviorComputer Science ApplicationsComputational Theory and MathematicsDiscriminative modelDiscriminant function analysisMultilayer perceptronNeural Networks ComputerArtificial intelligencebusinessInformation SystemsMathematicsJournal of Chemical Information and Computer Sciences
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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…

PharmacologyArtificial neural networkChemistryComputer scienceValue (computer science)Biological activityGeneral MedicineGeneral ChemistryInterval (mathematics)Function (mathematics)TopologyPlot (graphics)Computer Science ApplicationsSet (abstract data type)Structure-Activity RelationshipPharmaceutical PreparationsComputational Theory and MathematicsDiscriminative modelData DisplayNeural Networks ComputerInformation SystemsJournal of Chemical Information and Computer Sciences
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