AI in drug discovery: A wake-up call

Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis

Objective

To systematically map the knowledge landscape and development trends in artificial intelligence (AI) applications for antimicrobial resistance (AMR) research through bibliometric analysis, providing evidence-based insights to guide future research directions and inform strategic decision-making in this dynamic field.

Methods

A comprehensive bibliometric analysis was performed using the Web of Science Core Collection database for publications from 2014 to 2024. The analysis integrated multiple bibliometric approaches: VOSviewer for visualization of collaboration networks and research clusters, CiteSpace for temporal evolution analysis, and quantitative analysis of publication metrics. Key bibliometric indicators including co-authorship patterns, keyword co-occurrence, and citation impact were analyzed to delineate research evolution and collaboration patterns in this domain.

Results

A collection of 2,408 publications was analyzed, demonstrating significant annual growth with publications increasing from 4 in 2014 to 549 in 2023 (22.7% of total output). The United States (707), China (581), and India (233) were the leading contributors in international collaborations. The Chinese Academy of Sciences (53), Harvard Medical School (43), and University of California San Diego (26) were identified as top contributing institutions. Citation analysis highlighted two major breakthroughs: AlphaFold's protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). Keyword analysis identified six enduring research clusters from 2014 to 2024: sepsis, artificial neural networks, antimicrobial resistance, antimicrobial peptides, drug repurposing, and molecular docking, demonstrating the sustained integration of AI in antimicrobial therapy development. Recent trends show increasing application of AI technologies in traditional approaches, particularly in MALDI-TOF MS for pathogen identification and graph neural networks for large-scale molecular screening.

Conclusion

This bibliometric analysis shows the importance of artificial intelligence in enhancing the progress in the discovery of antimicrobial drugs especially toward the fight against AMR. From enhancing the fast, efficient and predictive performance of drug discovery methods, current AI capabilities have revealed observable potential to be proactive in combating the ever-growing challenge of AMR worldwide. This study serves not only an identification of current trends, but also, and especially, offers a strategic approach to further investigations.

A personal view on the history of toxins: From ancient times to artificial intelligence

Bioactive substances found in plants, microorganisms and animals have fascinated mankind since time immemorial. This review will focus on the progress that has been made over the centuries and our growing insights. The developments relate to both the discovery and characterization of novel bioactive substances, as well as the ceaseless implementation of refined techniques, the use of high-end instruments and breakthroughs in artificial intelligence with deep learning-based computational methods. As these approaches possess great translational potential, with many applications in different fields, such as therapeutic, diagnostic and agrochemical use, there is a good rationale to continue investing in toxinology-related research.

Preventing AI From Creating Biochemical Threats

We have previously applied our machine learning models for bioactivity and toxicity along with a generative algorithm to develop VX and tens of thousands of analogues. The publication brought attention to the ease of designing chemical warfare agents. In this Viewpoint, we discuss 10 recommendations to prevent future biochemical threats.