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September 10, 2025International Journal of Environmental Sciences

A Decade Of AI-Accelerated Drug Discovery Against Antimicrobial Resistance (2015–2025): Insights And Future Directions

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Authors

PMPakiso Moses Makhoahle

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Overview

Systematic review highlights AI's role in drug discovery and diagnostics for antimicrobial resistance, indicating areas for improvement.

Key Points

  • AI has enhanced the identification of novel antimicrobial compounds, accelerating drug discovery.
  • Machine learning models improved predictive accuracy for antimicrobial resistance patterns, facilitating intervention.
  • Systematic review synthesized literature across five databases, revealing critical insights into AI's application in AMR.
  • Challenges persist in data management and ethical concerns, calling for integrated AI frameworks and policy support.

Cite This Study

Pakiso Moses Makhoahle (2025) studied this question.

synapsesocial.com/papers/68c2416eb210217d6479f1fahttps://doi.org/10.64252/aexcea94
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  1. 1AI-Driven Strategies to Forecast and Combat Antibiotic Resistance2025
  2. 2A Review Delving through Current Challenges in AMR and Using Artificial Intelligence in Dealing with AMR2025
  3. 3Mitigating antimicrobial resistance by innovative solutions in AI (MARISA): a modified James Lind Alliance analysis2025
  4. 4Advancements in Microbial Drug Discovery: Leveraging AI, CRISPR, and Microbiome Insights to Overcome Antimicrobial Resistance2025
  5. 5MODELLING AND MITIGATING ANTIMICROBIAL RESISTANCE (AMR) THROUGH DATA-DRIVEN SURVEILLANCE, AI-POWERED DRUG DISCOVERY, AND PUBLIC HEALTH INTERVENTION DESIGN2025