6533b855fe1ef96bd12b0868
RESEARCH PRODUCT
Artificial neural network applied to prediction of fluorquinolone antibacterial activity by topological methods.
Tomás-vert FFacundo Pérez-giménezF. J. García-marchM. T. Salabert-salvadorJ. Jaén-oltrasubject
Quantitative structure–activity relationshipArtificial neural networkBasis (linear algebra)ChemistryMicrobial Sensitivity TestsTopologySet (abstract data type)Structure-Activity RelationshipAnti-Infective AgentsDrug DiscoveryMolecular MedicineNeural Networks ComputerAntibacterial activityTopology (chemistry)AlgorithmsAntibacterial agentFluoroquinolonesdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2000-03-29 | Journal of medicinal chemistry |