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Development of Antibiotic Resistance01:30

Development of Antibiotic Resistance

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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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Among the three main modes of HGT—transformation, conjugation, and transduction—transduction is unique in that it is mediated by bacteriophages, or bacterial viruses.Transduction occurs in two ways. Generalized transduction occurs during the lytic cycle of a bacteriophage infection. In this process, bacteriophages infect bacterial cells, replicate within them, and ultimately cause cell lysis, releasing newly assembled virions. Occasionally, random fragments of the bacterial genome...
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Gene Regulation in Microbial Communities: Quorum Sensing01:28

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Quorum sensing is a mechanism of bacterial communication that enables coordinated gene expression in response to changes in population density. This facilitates collective behaviors that enhance survival, resource acquisition, and ecological adaptation. This process relies on small signaling molecules called autoinducers that accumulate as bacterial populations grow. When a critical threshold concentration of autoinducers is reached, bacterial cells collectively modify gene expression,...
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Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
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Deep Learning and Antibiotic Resistance.

Stefan Lucian Popa1, Cristina Pop2, Miruna Oana Dita3

  • 12nd Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania.

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Summary

Artificial intelligence (AI) accelerates the discovery of new antibiotics to combat rising antibiotic resistance (AR). AI techniques like machine learning and deep learning streamline drug development, aiding researchers in finding novel solutions against bacterial infections.

Keywords:
adaptive resistanceantibiotic developmentantibiotic resistanceartificial intelligence (AI)automated antibiotic discoverycomputer-aided drug discoverydeep learningfuture of medicinemachine learningneural networks

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Area of Science:

  • Microbiology
  • Computational Chemistry
  • Pharmacology

Background:

  • Antibiotic resistance (AR) is a natural phenomenon exacerbated by modern practices like antibiotic overuse and decreased pharmaceutical research.
  • The high cost of antibiotic development hinders the discovery of new drugs.
  • Urgent need for novel strategies to combat escalating bacterial resistance.

Purpose of the Study:

  • To review emerging artificial intelligence (AI) techniques for identifying new antibiotics.
  • To highlight AI's role in accelerating the preclinical drug discovery phase.
  • To showcase AI's potential in overcoming the challenge of antibiotic resistance.

Main Methods:

  • Application of machine learning (ML), including neural networks (NN) and deep learning (DL), for rapid substance generation.
  • Utilizing text mining systems with DL algorithms for efficient data curation.
  • Combining quantitative structure-activity relationship (QSAR) with ML, and Raman spectroscopy/MALDI-TOF MS with NN for antibiotic identification.

Main Results:

  • AI significantly shortens the preclinical phase of antibiotic development.
  • AI facilitates rapid generation and identification of potential antibiotic candidates.
  • Integrated AI approaches offer faster and more interpretable results in antibiotic discovery.

Conclusions:

  • AI techniques are crucial tools for researchers and clinicians in the fight against antibiotic resistance.
  • AI accelerates the identification of new antibiotics, addressing a critical gap in drug development.
  • The integration of AI promises to enhance the speed and efficiency of discovering novel antibacterial agents.