Discovery of Novel Antimicrobial Peptides Using Machine Learning and Molecular Dynamic Simulations
A special issue of Antibiotics (ISSN 2079-6382). This special issue belongs to the section "Antimicrobial Peptides".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 142
Special Issue Editors
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Special Issue Information
Dear Colleagues,
Antimicrobial peptides are a class of small molecules composed of peptides with antimicrobial activity, possessing unique mechanisms against various pathogens, and are considered one of the important directions in future antimicrobial drug research. Traditional methods for screening and designing antimicrobial peptides suffer from issues such as high costs, low efficiency, and time consumption. In contrast, through data-driven machine learning methods, researchers can utilize large-scale biological data for pattern recognition and prediction, thereby accelerating the process of discovering antimicrobial peptides. Additionally, molecular dynamics simulation techniques can simulate the interactions between antimicrobial peptides and target molecules, revealing their structural and functional features at the molecular level and providing crucial insights for designing antimicrobial peptides with enhanced selectivity and efficacy. This Special Issue aims to explore the acceleration of the discovery of novel antimicrobial peptides and their antimicrobial mechanism research using machine learning and molecular dynamics simulation. The goal is to leverage computational biology techniques to expedite the discovery and development of safer and more effective antimicrobial peptide drugs, bringing new hope and possibilities to human health and the field of antimicrobial therapy.
We welcome manuscripts that use computational screening tools to identify the potential activity of novel antimicrobial peptides and confirm them experimentally. We also encourage manuscripts that focus on the development of novel computational screening pipelines or tools and provide supporting data to demonstrate the validity of their approach.
We look forward to receiving your manuscripts and collaborating with you on an impactful Special Issue.
Dr. Lei Zhao
Dr. Liang Zhao
Dr. Fei Pan
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Antibiotics is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- antimicrobial peptide
- computational design
- machine learning
- deep learning
- molecular dynamic simulations
- de novo generation
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