Post-publication cover of Molecular Topology for the Search of New Anti-MRSA Compounds

*Note: Mol2Net conference is associated to different MDPI journals special issues guest edited by Mol2Net Conference Committee members. This is an strategy to increase the online post-publication visibility of papers and conference, promote post-publication brainstorming discussion, and increase authors feedback. This association implies that our conference perform post-publication indexing of selected papers already published in MDPI journals with the consent of the issue editors. We publish free-of-cost these post-publication summaries. They include a shortened title, corresponding author info, and paper cover pdf file. The cover pdf file contains paper first page with all authors, abstract, full reference , and link to original papers.

Reference: This is a Mol2Net conference post-publication cover for a paper published in the special issue Complex Networks, Bio-Molecular Systems, and Machine Learning, Edited by: Dr. H González-Díaz. Visit the link to see original paper. Reference: Int. J. Mol. Sci. 2021, 22(11), 5823; https://doi.org/10.3390/ijms22115823

Abstract. 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 DF_MRSA1, DF_MRSA2, DF_MRSA3 and DF_MRSA4 (DF_MRSA 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.