*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 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.
Now we closed the publication phase and launched the post-publication phase of the conference. REVIEWWWERS'08 Brainstorming Workshop is Now Open from 2023-Jan-01 to 2023-Jan-31. MOL2NET Committee, Authors, and Validated Social Media Followers Worldwide are invited to Post Moderated Questions/Answers, Comments, about papers.
Please kindly post your public Answers (A) to the following questions in order to promote interchange of scientific ideas.
These are my Questions (Q) to you, please kindly post your public Answers (A) below to promote scientific discussion and training of conference readers :
Q1. Why were only in vitro tests conducted according to the criteria of the CLSI considered as valid?
Q2. Why were compounds with MICs between 1 and 16 mg/mL excluded from the study?
Dear author thanks in advance for your kind support answering the questions. Now, please become a verified REVIEWWWER of our conference by making questions to other papers in different Mol2Net congresses. Commenting Steps: Login, Go to Papers List, Select Paper, Write Comment, Click Post Comment.
Papers list: https://mol2net-08.sciforum.net/presentations/view
Workshop link: https://mol2net-08.sciforum.net/#reviewwwers
Thank you and the rest of editors for having accepted my work in your prestigious congress/workshop.
In response to your first question, the reason why we chose in vitro data was mainly due to the unexisting in vivo data for compounds who have shown scarce or no activity in vitro. Activity data from antibacterial AND non-antibacterial compounds is vital for conducting an LDA study.
In response to your second question, we wanted to find a topological pattern to successfully classify compounds as antibacterial or non-antibacterial, so we decided not to consider compounds with intermediate in vitro activity (1-16 mg/L) which could lead to non-ideal QSAR outcomes.
I’ll be more than happy to comment on other authors’ publications in order to enhance scientific discussion.
Kind regards,
Dr. Jose I. Bueso-Bordils
Thank you and the rest of editors for having accepted my work in your prestigious congress/workshop.
In response to your first question, the reason why we chose in vitro data was mainly due to the unexisting in vivo data for compounds who have shown scarce or no activity in vitro. Activity data from antibacterial AND non-antibacterial compounds is vital for conducting an LDA study.
In response to your second question, we wanted to find a topological pattern to successfully classify compounds as antibacterial or non-antibacterial, so we decided not to consider compounds with intermediate in vitro activity (1-16 mg/L) which could lead to non-ideal QSAR outcomes.
I’ll be more than happy to comment on other authors’ publications in order to enhance scientific discussion.
Kind regards,
Dr. Jose I. Bueso-Bordils
