Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 22, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/ijms22115823
Keywords
antibacterial; antibiotics; anti-MRSA; computational chemistry; linear discriminant analysis; molecular topology; molecular connectivity; topological indices; quinolones; QSAR
Funding
- ICB (Biomedical Science Institute of the CEU-Cardenal Herrera University) [BC/ICB-Santander 05/12, BC/ICB-Santander 06/12]
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In this study, discriminant equations were developed using molecular topology and linear discriminant analysis (LDA) to reliably predict the anti-MRSA activity of organic compounds. The results demonstrated the high efficiency of combining molecular topology with LDA for predicting anti-MRSA activity.
The variability of methicillin-resistant Staphylococcus aureus (MRSA), its rapid adaptive response against environmental changes, and its continued acquisition of antibiotic resistance determinants have made it commonplace in hospitals, where it causes the problem of multidrug resistance. In this study, we used molecular topology to develop several discriminant equations capable of classifying compounds according to their anti-MRSA activity. Topological indices were used as structural descriptors and their relationship with anti-MRSA activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four extra equations were constructed, named DFMRSA1, DFMRSA2, DFMRSA3 and DFMRSA4 (DFMRSA was built in a previous study), all with good statistical parameters, such as Fisher-Snedecor F (>68 in all cases), Wilk's lambda (<0.13 in all cases), and percentage of correct classification (>94% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of anti-MRSA activity.
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