Artificial Intelligence as a Tool for Combating Antimicrobial Resistance

Dear Colleagues,

Antimicrobial resistance (AMR) is a growing global threat that undermines the effectiveness of existing treatments, leading to increased mortality and economic burdens. Artificial intelligence (AI) presents a transformative opportunity to combat AMR through enhanced surveillance, accelerated drug discovery, and optimized antimicrobial usage. By strategically leveraging AI technologies, we can develop proactive solutions to mitigate the impact of AMR and strengthen global healthcare resilience. AI can enhance AMR surveillance by analyzing vast datasets from clinical records, laboratory results, and genomic databases. Machine learning algorithms can identify patterns, predict resistance trends, and provide real-time alerts for emerging threats. This capability enables early intervention and improved decision-making in healthcare settings. AI-driven models can expedite drug discovery by identifying potential antimicrobial candidates more efficiently than traditional methods. By utilizing molecular dynamics simulations and AI-powered screening techniques, researchers can discover novel drugs, optimize existing compounds, and reduce the time and cost required to bring new antimicrobials to market. AI can optimize antimicrobial prescribing practices by providing clinicians with decision support systems that recommend appropriate treatments based on patient-specific data. This reduces the misuse of antibiotics, slows resistance development, and improves patient outcomes. AI-driven predictive analytics can also assist in identifying cases where alternative therapies may be more effective, further promoting responsible antimicrobial use. To effectively harness AI for AMR mitigation, a structured and collaborative approach is necessary: engaging governments, healthcare providers, researchers, and industry partners to align efforts and facilitate data sharing, establishing a consortium to develop standardized data collection and sharing protocols, ensuring interoperability and consistency across platforms, and providing hands-on training in AI tools, including molecular dynamics, Python, and R, to equip researchers and healthcare professionals with the necessary skills. By integrating AI into AMR strategies, we expect to achieve enhanced surveillance with improved accuracy and timeliness in tracking AMR trends, enabling proactive interventions, accelerated drug discovery with the faster identification and development of novel antimicrobials, reducing costs and improving market readiness, optimized antimicrobial use through a reduction in inappropriate antibiotic prescriptions, slowing the rate of resistance development, and strengthened global collaboration facilitated by AI-driven insights and innovations. AI has the potential to revolutionize the fight against AMR by enhancing data analysis, expediting drug development, and promoting responsible antimicrobial use. Through interdisciplinary collaboration and the adoption of AI-driven solutions, we can make significant strides in addressing this global health challenge.

This Special Issue will tackle, but is not limited to, the following issues:

• AI/ML applications for AMR;
• AI applications considering MD or docking (in datasets);
• LLM applications for AMR;
• AI-driven models for drug discovery;
• AI-driven models for compound property detection/discovery;
• Novel augmentation techniques applied in AMR.

As Guest Editors of this Special Issue, we look forward to reviewing your submissions.

Dr. Sergi Maicas
Dr. Jaume Segura-Garcia
Guest Editors

Manuscript Submission Information

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• AI
• AMR
• drug discovery
• datasets
• antibiotics