title: Designing antibacterial compounds through a topological substructural approach.
abstract: A novel application of TOPological Substructural MOlecular DEsign (TOPS-MODE) was carried out in antibacterial drugs using computer-aided molecular design. Two series of compounds, one containing antibacterial and the other containing non-antibacterial compounds, were processed by a k-means cluster analysis in order to design training and predicting series. All clusters had a p-level < 0.005. Afterward, a linear classification function has been derived toward discrimination between antibacterial and non-antibacterial compounds. The model correctly classifies 94% of active and 86% of inactive compounds in the training series. More specifically, the model showed a global good classification of 91%, i.e., 263 cases out of 289. In predicting series, the model has shown overall predictabilities of 91 and 83% for active and inactive compounds, respectively. Thereby, the model has a global percentage of good classification of 89%. The TOPS-MODE approach, also, similarly compares with respect to one of the most useful models for antimicrobials selection reported to date.
authors.affiliation: Department of Chemistry and Pharmacy, Faculty of Engineering Chemistry and Pharmacy, University of Camagüey, 74650 Camagüey, Cuba.
authors.forename: Enrique
authors.initials: E
authors.lastname: Molina
authors.forename: Humberto González
authors.initials: HG
authors.lastname: Díaz
authors.forename: Maykel Pérez
authors.initials: MP
authors.lastname: González
authors.forename: Elismary
authors.initials: E
authors.lastname: Rodríguez
authors.forename: Eugenio
authors.initials: E
authors.lastname: Uriarte
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000138:chemical synthesis* / Q000145:classification / Q000494:pharmacology*; D016000:Cluster Analysis; D015195:Drug Design; D005103:Expert Systems; D015233:Models, Statistical; D021281:Quantitative Structure-Activity Relationship
chemical_list: D000900:Anti-Bacterial Agents