6533b852fe1ef96bd12aadcb

RESEARCH PRODUCT

Antimicrobial Activity Characterization in a Heterogeneous Group of Compounds

Ramón García-domenechJ.v. De Julián-ortiz

subject

Heterogeneous groupMolecular StructureArtificial neural networkbusiness.industryLinear modelDiscriminant AnalysisPattern recognitionGeneral ChemistryLinear analysisAntimicrobialLinear discriminant analysisPattern Recognition AutomatedComputer Science ApplicationsAnti-Infective AgentsNonlinear DynamicsComputational Theory and MathematicsTest setPattern recognition (psychology)Linear ModelsNeural Networks ComputerArtificial intelligencebusinessInformation SystemsMathematics

description

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.

https://doi.org/10.1021/ci9702454