Artificial neural network applied to prediction of fluorquinolone antibacterial activity by topological methods.
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.
Jaén-Oltra
J
J
Department of Physical Chemistry, University of València, Av. Vicent Andrés Estellés S/N, 46100 Burjassot, Valencia, Spain.
Salabert-Salvador
M T
MT
García-March
F J
FJ
Pérez-Giménez
F
F
Tomás-Vert
F
F
D000465:Algorithms; D000890:Anti-Infective Agents / Q000737:chemistry* / Q000494:pharmacology; D024841:Fluoroquinolones; D008826:Microbial Sensitivity Tests; D016571:Neural Networks, Computer; D013329:Structure-Activity Relationship
D000890:Anti-Infective Agents; D024841:Fluoroquinolones