In silico Antibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach

Simple item page
dc.contributor.authorCastillo-Garit, Juan A.
dc.contributor.authorMarrero-Ponce, Yovani
dc.contributor.authorBarigye, Stephen J.
dc.contributor.authorMedina-Marrero, Ricardo
dc.date.accessioned2023-02-09T17:12:27Z
dc.date.available2023-02-09T17:12:27Z
dc.date.issued2015
dc.description.abstractIn the recent times, the race to cope with the increasing multidrug resistance of pathogenic bacteria has lost much of its momentum and health professionals are grasping for solutions to deal with the unprecedented resistance levels. As a result, there is an urgent need for a concerted effort towards the development of new antimicrobial drugs to stay ahead in the fight against the ever adapting bacteria. In the present report, antibacterial classification functions (models) based on the topological molecular computational design-computer aided ‘‘rational’’ drug design (TOMOCOMD-CARDD) atom-based non-stochastic and stochastic bilinear indices are presented. These models were built using the linear discriminant analysis (LDA) method over a balanced chemical compounds dataset of 2230 molecular structures, with a diverse range of structural and molecular mechanism modes. The results of this study indicated that the non-stochastic and stochastic bilinear indices provided excellent classification of the chemical compounds (with accuracies of 86.31% and 84.92%, respectively, in the training set). These models were further externally validated yielding correct classification percentages of 86.55% and 87.91% for the non-stochastic and stochastic bilinear models, respectively. Additionally, the obtained models were compared with those reported in the literature and demonstrated comparable results, although the latter were built over much smaller datasets and with much higher degrees of freedom. Finally, simulated ligand-based virtual screening of 116 compounds, recently identified as potential antibacterials, was performed yielding 86.21% and 83.62% of correct classification, respectively, and thus demonstrating the utility of the obtained TOMOCOMD-CARDD models in the search of novel compounds with desirable antibacterial activity.en_US
dc.identifier.citationCastillo-Garit, J. A., Marrero-Ponce, Y., Barigye, S. J., Medina-Marrero, R., Bernal, M. G., de la Vega, J. M., ... & Acevedo-Barrios, R. (2015). In silico antibacterial activity modeling based on the TOMOCOMD-CARDD approach. Journal of the Brazilian Chemical Society, 26, 1218-1226.https://doi.org/10.5935/0103-5053.20150087en_US
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7707
dc.language.isoenen_US
dc.publisherJournal of the Brazilian Chemical Societyen_US
dc.subjectTOMOCOMD-CARDD softwareen_US
dc.subjectatom-based bilinear indexen_US
dc.subjectlinear discriminant analysisen_US
dc.subjectantibacterial activityen_US
dc.subjectQSARen_US
dc.titleIn silico Antibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approachen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
In silico Antibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach.pdf
Size:
269.71 KB
Format:
Adobe Portable Document Format
Description:
In silico Antibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Engineering and Technology