title: Antimicrobial activity characterization in a heterogeneous group of compounds.
abstract: In this work we carry out a study of pattern recognition to detect the microbiological activity in a group of heterogeneous compounds. The structural descriptors utilized are the topological connectivity indexes. The methods followed are stepwise linear discriminant analysis (linear analysis) and artificial neural network (nonlinear analysis). Although both methods are appropriate to differentiate between active and inactive compounds, the artificial neural network is, in this case, more adequate, since it shows in a test set a prediction success of 98%, versus 92% obtained with linear discriminant analysis.
authors.affiliation: Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Spain. ramon.garcia@uv.es
authors.forename: R
authors.initials: R
authors.lastname: García-Domenech
authors.forename: J V
authors.initials: JV
authors.lastname: de Julián-Ortiz
mesh_terms: D000890:Anti-Infective Agents / Q000737:chemistry* / Q000494:pharmacology*; D016002:Discriminant Analysis; D016014:Linear Models; D015394:Molecular Structure; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated
chemical_list: D000890:Anti-Infective Agents