Designing Antibacterial Compounds through a Topological Substructural Approach

• Published in: Journal of Chemical Information and Computer Sciences 2004-03, Vol.44 (2), p.515-521
• Main Authors: Molina, Enrique, Díaz, Humberto González, González, Maykel Pérez, Rodríguez, Elismary, Uriarte, Eugenio
• Format: Article
• Language: English
• Subjects: Algorithms; Anti-Bacterial Agents - chemical synthesis; Anti-Bacterial Agents - classification; Anti-Bacterial Agents - pharmacology; Bacteria; Chemical compounds; Cluster Analysis; Computer based modeling; Drug Design; Expert Systems; Models, Statistical; Molecular structure; Quantitative Structure-Activity Relationship; Topology
• ISSN: 0095-2338; 1549-9596; 1549-960X
• DOI: 10.1021/ci0342019

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.