Artificial Neural Networks and Linear Discriminant Analysis:  A Valuable Combination in the Selection of New Antibacterial Compounds

• Published in: Journal of Chemical Information and Computer Sciences 2004-05, Vol.44 (3), p.1031-1041
• Main Authors: Murcia-Soler, Miguel, Pérez-Giménez, Facundo, García-March, Francisco J, Salabert-Salvador, Ma Teresa, Díaz-Villanueva, Wladimiro, Castro-Bleda, María José, Villanueva-Pareja, Angel
• Format: Article
• Language: English
• Subjects: Anti-Bacterial Agents - chemistry; Anti-Bacterial Agents - pharmacology; Bacteria; Discriminant Analysis; Microbial Sensitivity Tests; Neural networks; Neural Networks (Computer)
• ISSN: 0095-2338; 1549-9596; 1549-960X
• DOI: 10.1021/ci030340e

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 identification of antibacterial agents. The results confirmed the discriminative capacity of the topological descriptors proposed. The combined use of LDA and MLP in the guided search and the selection of new structures with theoretical antibacterial activity proved highly effective, as shown by the in vitro activity and toxicity assays conducted.