Modern Strategies in Antibacterial Discovery: Integrating In Silico Predictions with In Vitro Validation

A special issue of Antibiotics (ISSN 2079-6382). This special issue belongs to the section "The Global Need for Effective Antibiotics".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 899

Editor

The Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
Interests: antibiotics; drug design; Mur ligases; cell wall; medicinal chemistry; peptidoglycan; computer-aided drug design; machine learning
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Special Issue Information

Dear Colleagues,

The global rise of antimicrobial resistance (AMR) continues to outpace the development of traditional antibiotics, necessitating a paradigm shift in how we discover and optimize new antibacterial scaffolds. As we look toward 2030 and beyond, the integration of computational intelligence with robust synthetic methodology is no longer optional; it is essential.

This Special Issue of Antibiotics aims to showcase a holistic view of modern antibacterial research. We seek to bridge the gap between theoretical prediction and laboratory reality. We invite original research and comprehensive reviews that traverse the entire drug discovery pipeline, including:

  • Computational Frontiers: Utilizing machine learning (ML), deep learning, and advanced molecular docking for target prediction and virtual screening.
  • Synthetic Innovation: New methodologies in organic synthesis, including the development of novel heterocyclic scaffolds, natural product total synthesis, and late-stage functionalization of known antibacterial agents.
  • Targeted Analysis: Identification of novel bacterial targets (e.g., cell wall biosynthetic enzymes, virulence factors, or metabolic pathways) and the subsequent validation through biochemical assaying.
  • Integrated Platforms: Studies that combine in silico hit identification with subsequent chemical synthesis and rigorous in vitro/in vivo biological evaluation.

By bringing together chemists, bioinformaticians, and microbiologists, this Special Issue will serve as a multidisciplinary forum for the latest breakthroughs in the fight against resistant pathogens. We look forward to receiving your contributions.

Dr. Rok Frlan
Guest Editor

Manuscript Submission Information

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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-anonymized 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

  • artificial intelligence/machine learning in drug discovery
  • total synthesis & methodology lead optimization
  • target identification & validation
  • structure-activity relationship (SAR)
  • enzymatic assays
  • molecular modeling
  • drug-resistant pathogens

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Published Papers (1 paper)

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Review

76 pages, 4443 KB  
Review
Smart Nano-Antibiotics: AI-Guided Stimuli-Responsive Nanoplatforms for Precision Antimicrobial Therapy
by Nargish Parvin, Keunhwan Park, Jae Hak Jung and Tapas Kumar Mandal
Antibiotics 2026, 15(7), 638; https://doi.org/10.3390/antibiotics15070638 - 26 Jun 2026
Viewed by 651
Abstract
The rapid rise of antimicrobial resistance (AMR) has created an urgent need for innovative therapeutic strategies beyond conventional antibiotics. Smart nano-antibiotics have emerged as advanced antimicrobial systems capable of improving drug delivery, enhancing pathogen targeting, overcoming biofilm-associated resistance, and reducing systemic toxicity. This [...] Read more.
The rapid rise of antimicrobial resistance (AMR) has created an urgent need for innovative therapeutic strategies beyond conventional antibiotics. Smart nano-antibiotics have emerged as advanced antimicrobial systems capable of improving drug delivery, enhancing pathogen targeting, overcoming biofilm-associated resistance, and reducing systemic toxicity. This review discusses recent progress in stimuli-responsive nanoplatforms, including pH-responsive, enzyme-responsive, temperature-sensitive, and redox-activated systems for precision antimicrobial therapy. The role of artificial intelligence in nanomaterial design, toxicity prediction, drug release optimization, and personalized treatment development is also critically examined. Furthermore, the review highlights targeted antimicrobial delivery, multifunctional nano-drug combination systems, biosensor integration, and autonomous AI-driven therapeutic platforms for combating multidrug-resistant infections. Current challenges related to toxicity, regulatory limitations, scalability, and AI data reliability are discussed alongside emerging clinical and industrial developments. Smart nano-antibiotics represent a promising next-generation approach for improving precision antimicrobial therapy and addressing the growing global burden of antimicrobial resistance. Full article
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