Curated Papers > Recent high-impact research on "AI Drug Discovery" (IF≥30, last 5 years)

Artificial intelligence and infectious diseases: tackling antimicrobial resistance, from personalised care to antibiotic discovery.

Literature Information

DOI	10.1016/S1473-3099(25)00313-5
PMID	40972630
Journal	The Lancet. Infectious diseases
Impact Factor	31.0
JCR Quartile	Q1
Publication Year	2025
Times Cited	0
Keywords	Antimicrobial Resistance, Artificial Intelligence, Drug Discovery, Public Health
Literature Type	Journal Article, Review
ISSN	1473-3099
Authors	Alex Howard, Nada Reza, Peter L Green, Mo Yin, Erin Duffy, Henry C Mwandumba, Alessandro Gerada, William Hope

TL;DR

This research discusses the pressing challenge of antimicrobial resistance (AMR) and the UN General Assembly's targets for addressing its impact on health systems, emphasizing the potential role of artificial intelligence (AI) in enhancing drug discovery, stewardship, and surveillance. Despite AI's promise in revealing hidden data patterns and improving clinical decision-making, significant barriers in infrastructure, expertise, and implementation strategies hinder its effective application in combating AMR.

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Antimicrobial Resistance · Artificial Intelligence · Drug Discovery · Public Health

Abstract

Antimicrobial resistance (AMR) is an intractable problem that has the potential to significantly limit advances in human health. Recently, the UN General Assembly (UNGA) High-Level Statement on AMR defined targets for addressing the impact of resistance on human, animal, and environmental health. For human health, the discovery and development of new antibiotics, antimicrobial stewardship programmes, antimicrobial surveillance, and infection control and prevention are all key areas. Artificial intelligence (AI) is ideally placed to help achieve the UNGA targets via its role in revealing patterns in data that are clinically indiscernible, and using that information to build clinical decision support systems. However, significant barriers remain in terms of necessary infrastructure, know-how, and the implementation of AI approaches. In this Series paper, we consider the potential applications of AI in combatting the AMR problem through drug discovery and development, antimicrobial stewardship, diagnostics, and surveillance, and their use in public health. We then discuss the technical, infrastructure, regulatory, ethical, and policy challenges that affect these domains.

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Primary Questions Addressed

1. How can AI be integrated into existing antimicrobial stewardship programmes to enhance their effectiveness?
2. What specific AI techniques are being explored for the discovery of new antibiotics?
3. In what ways can AI contribute to improving diagnostics for infections caused by resistant pathogens?
4. What role does data sharing play in overcoming the barriers to implementing AI solutions in public health related to AMR?
5. How can ethical considerations be addressed when deploying AI technologies in the fight against antimicrobial resistance?

Key Findings

Research Background and Objectives

Antimicrobial resistance (AMR) poses a critical threat to global health, hindering progress in medical advancements. The UN General Assembly (UNGA) has outlined specific targets to mitigate the effects of AMR on human, animal, and environmental health. This paper aims to explore how artificial intelligence (AI) can be leveraged to address AMR through various applications, including drug discovery, antimicrobial stewardship, diagnostics, and public health surveillance.

Main Methods/Materials/Experimental Design

The paper discusses several potential applications of AI in combating AMR, emphasizing the need for a structured approach. The following diagram outlines the technical route for implementing AI in AMR solutions:

1. Drug Discovery and Development: Utilizing AI to identify new antibiotics and optimize existing ones by recognizing patterns in biological data.
2. Antimicrobial Stewardship: Implementing AI-driven clinical decision support systems to improve prescribing practices and minimize resistance development.
3. Diagnostics: Enhancing diagnostic capabilities through AI to provide rapid and accurate identification of infections.
4. Surveillance: Developing real-time surveillance systems to monitor AMR trends and outbreaks effectively.

Key Results and Findings

• AI has the potential to reveal clinically significant patterns in vast datasets, which can inform clinical practices and public health policies.
• Successful applications of AI in AMR can lead to more efficient drug discovery processes, improved patient outcomes through better stewardship, and enhanced diagnostic accuracy.
• Despite its potential, there are significant barriers to the integration of AI in AMR strategies, including:
  • Infrastructure: Lack of necessary technological frameworks.
  • Know-how: Insufficient expertise in AI methodologies among healthcare professionals.
  • Implementation: Challenges in applying AI solutions in real-world settings.

Main Conclusions/Significance/Innovation

The paper concludes that while AI holds great promise in addressing the multifaceted challenges posed by AMR, realizing its full potential requires overcoming substantial infrastructural, technical, and regulatory hurdles. The integration of AI into AMR strategies can revolutionize the way healthcare systems respond to antibiotic resistance, leading to better health outcomes and more sustainable practices.

Research Limitations and Future Directions

• Limitations: The paper acknowledges that the current understanding of AI applications in AMR is still in its infancy, with limited real-world examples and a need for further research to validate the proposed approaches.
• Future Directions:
  • Developing robust AI frameworks that can be adapted across various healthcare settings.
  • Conducting interdisciplinary research to bridge the gap between AI technology and clinical application.
  • Establishing clear regulatory guidelines to ensure ethical use of AI in healthcare, particularly concerning patient data and privacy.

Aspect	Current Status	Future Needs
Infrastructure	Lacking necessary technology	Development of AI-compatible systems
Expertise	Insufficient knowledge among clinicians	Training programs for healthcare professionals
Implementation	Limited real-world applications	Pilot projects to demonstrate effectiveness
Regulatory Framework	Unclear guidelines	Development of ethical standards

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