Leveraging artificial intelligence in the fight against infectious diseases

Are AI-based surveillance systems for healthcare-associated infections ready for clinical practice? A systematic review and meta-analysis

Healthcare-associated infections (HAIs) are a global public health concern, imposing significant clinical and financial burdens. Despite advancements, surveillance methods remain largely manual and resource-intensive, often leading to underreporting. In this context, automation, particularly through Artificial Intelligence (AI), shows promise in optimizing clinical workflows. However, adoption challenges persist. This study aims to evaluate the current performance and impact of AI in HAI surveillance, considering technical, clinical, and implementation aspects. We conducted a systematic review of Scopus and Embase databases following PRISMA guidelines. AI-based models' performances, accuracy, AUC, sensitivity, and specificity, were pooled using a random-effect model, stratifying by detected HAI type. Our study protocol was registered in PROSPERO (CRD42024524497). Of 2834 identified citations, 249 studies were reviewed. The performances of AI models were generally high but with significant heterogeneity between HAI types. Overall pooled sensitivity, specificity, AUC, and accuracy were respectively 0.835, 0.899, 0.864, and 0.880. About 35.7 % of studies compared AI system performance with alternative automated or standard-of-care surveillance methods, with most achieving better or comparable results to clinical scores or manual surveillance. <7.6 % explicitly measured AI impact in terms of improved patient outcomes, workload reduction, and cost savings, with the majority finding benefits. Only 30 studies deployed the model in a user-friendly tool, and 9 tested it in real clinical practice. In this systematic review, AI shows promising performance in HAI surveillance, although its routine application in clinical practice remains uncommon. Despite over a decade, retrieved studies offer scant evidence on reducing burden, costs, and resource use. This prevents their potential superiority over traditional or simpler automated surveillance systems from being fully evaluated. Further research is necessary to assess impact, enhance interpretability, and ensure reproducibility.

Harnessing advances in mechanisms, detection, and strategies to combat antimicrobial resistance

Antimicrobial resistance (AMR) is a growing global health crisis, threatening the effectiveness of antibiotics and other antimicrobial agents, leading to increased morbidity, mortality, and economic burdens. This review article provides a comprehensive analysis of AMR, beginning with a timeline of antibiotics discovery and the year of first observed resistance. Main mechanisms of AMR in bacteria, fungi, viruses, and parasites are summarized, and the main mechanisms of bacteria are given in detail. Additionally, we discussed in detail methods for detecting AMR, including phenotypic, genotypic, and advanced methods, which are crucial for identifying and monitoring AMR. In addressing AMR mitigation, we explore innovative interventions such as CRISPR-Cas systems, nanotechnology, antibody therapy, artificial intelligence (AI), and the One Health approach. Moreover, we discussed both finished and ongoing clinical trials for AMR. This review emphasizes the urgent need for global action and highlights promising technologies that could shape the future of AMR surveillance and treatment. By integrating interdisciplinary research and emerging clinical insights, this study aims to guide individuals toward impactful solutions in the battle against AMR.

Tutorial: guidelines for the use of machine learning methods to mine genomes and proteomes for antibiotic discovery

Genomes and proteomes constitute a rich reservoir of molecular diversity. However, they have remained underexplored because of a lack of appropriate tools. In recent years, computational approaches have been developed to mine this unexplored biological information, or dark matter, accelerating the discovery of new antibiotic molecules. Such efforts have yielded a wide range of new molecules. These include peptides released via predicted proteolytic cleavage of larger proteins, termed 'encrypted peptides', which have been found to be widespread in nature. Molecules encoded by and translated from small open reading frames within genomic sequences have also been uncovered, further expanding the landscape of bioactive compounds. Here, we discuss computational approaches, including machine learning and artificial intelligence (AI) tools, which have been used to date to identify antimicrobial compounds, with a special emphasis on peptides. We also propose potential avenues for future exploration in this rapidly evolving field. Moreover, we provide an overview of the experimental methods commonly used to validate these computational predictions. We anticipate that efforts combining cutting-edge AI and experimental approaches for biological sequence mining will reveal new insights into host immunity and continue to accelerate discoveries in the fields of antibiotics and infectious diseases.

Cutting-edge tools for unveiling the dynamics of plasmid-host interactions

The plasmid-mediated transfer of antibiotic resistance genes (ARGs) in complex microbiomes presents a significant global health challenge. This review examines recent technological advancements that have enabled us to move beyond the limitations of culture-dependent detection of conjugation and have enhanced our ability to track and understand the movement of ARGs in real-world scenarios. We critically assess the applications of single-cell sequencing, fluorescence-based techniques and advanced high-throughput chromatin conformation capture (Hi-C) approaches in elucidating plasmid-host interactions at unprecedented resolution. We also evaluate emerging techniques such as CRISPR-based phage engineering and discuss their potential for developing targeted strategies to curb ARG dissemination. Emerging data derived from these technologies have challenged our previous paradigms on plasmid-host compatibility and an awareness of an emerging uncharted realm for ARGs.

Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry

From accelerating simulations and exploring chemical space, to experimental planning and integrating automation within experimental labs, artificial intelligence (AI) is changing the landscape of chemistry. We are seeing a significant increase in the number of publications leveraging these powerful data-driven insights and models to accelerate all aspects of chemical research. For example, how we represent molecules and materials to computer algorithms for predictive and generative models, as well as the physical mechanisms by which we perform experiments in the lab for automation. Here, we present ten diverse perspectives on the impact of AI coming from those with a range of backgrounds from experimental chemistry, computational chemistry, computer science, engineering and across different areas of chemistry, including drug discovery, catalysis, chemical automation, chemical physics, materials chemistry. The ten perspectives presented here cover a range of themes, including AI for computation, facilitating discovery, supporting experiments, and enabling technologies for transformation. We highlight and discuss imminent challenges and ways in which we are redefining problems to accelerate the impact of chemical research via AI.

Role of artificial intelligence in advancing immunology

Artificial intelligence (AI) has revolutionized various biomedical fields, particularly immunology, by enhancing vaccine development, immunotherapies, and allergy treatments. AI helps identify potential vaccine candidates and predict how the body reacts to different antigens based on a vast number of genomic sequences and protein structures. AI can help cancer patients by analyzing their data and offering personalized immunotherapies. AI has also advanced the field of allergy by identifying potential allergens and predicting allergic reactions based on patient genetic and environmental factors. AI could also help diagnose multiple immunological diseases, including autoimmune diseases and immunodeficiencies, by analyzing patient history and laboratory results. AI has deepened our understanding of the human genome by providing numerous amounts of data from DNA sequences previously believed to be nonfunctional. Through machine learning and deep learning, many laborious research tasks, such as screening for DNA mutations, can be efficiently performed in a short amount of time. AI and machine learning are significantly advancing biomedical science in significant areas, including research and industry. This review discusses the latest AI-based tools that can be utilized in the field of immunology. AI tools significantly advance the field of medical research and healthcare by enabling new scientific discoveries and facilitating rapid clinical diagnosis.

Impact of Multiplex PCR on Diagnosis of Bacterial and Fungal Infections and Choice of Appropriate Antimicrobial Therapy

Multiplex Polymerase Chain Reaction (PCR) has significantly impacted the field of infectious disease diagnostics, offering rapid and precise identification of bacterial and fungal pathogens. Unlike traditional culture methods, which may take days to yield results, multiplex PCR provides diagnostic insights within hours, enabling faster, targeted antimicrobial therapy and reducing the delay in treating critical infections like sepsis. The technique's high sensitivity and broad pathogen coverage make it ideal for both single and polymicrobial infections, improving outcomes across respiratory, bloodstream, and bacterial/fungal infections. However, multiplex PCR is not without challenges; initial high costs and the need for specialized training can limit its adoption, especially in low-resource settings. This review discusses the clinical advantages and limitations of multiplex PCR, highlighting its influence on diagnostic accuracy, antimicrobial stewardship, and the global fight against antimicrobial resistance (AMR). Furthermore, recent innovations in multiplex PCR, such as digital PCR and portable devices, are explored as potential tools for expanding access to rapid diagnostics worldwide.

Comprehensive lifecycle quality control of medical data - automated monitoring and feedback mechanisms based on artificial intelligence

BackgroundDigital healthcare's advance has underscored an urgent requirement for solid medical record quality control, critical for data integrity, surpassing manual methods' inadequacies.ObjectiveThe goal was to develop an AI system to manage medical record quality control comprehensively, using advanced AI like reinforcement learning and NLP to boost management's precision and efficiency.MethodsThis AI system uses a closed-loop framework for real-time record review using natural language processing techniques and reinforcement learning, synchronized with the hospital information system. It features a data layer for monitoring, a service layer for AI analysis, and a presentation layer for user engagement. Its impact was evaluated by comparing quality metrics pre- and post-deployment.ResultsWith the AI system, quality control became fully operational, with review times per record plummeting from 4200 s to 2 s. The share of Grade A records rose from 89.43% to 99.21%, and the system markedly minimized formal and substantive record errors, enhancing completeness and accuracy. The implementation of the artificial intelligence-based medical record quality control system optimizes the quality control process, dynamically regulates the diagnostic behavior of medical staff, and promotes the standardization and normalization of clinical medical record writing.ConclusionsThe AI-driven system significantly upgraded the management of medical records in terms of efficiency and accuracy. It provides a scalable approach for hospitals to refine quality control, propelling healthcare towards heightened intelligence and automation, and foreshadowing AI's pivotal role in future healthcare quality management.

An explainable deep learning platform for molecular discovery

Deep learning approaches have been increasingly applied to the discovery of novel chemical compounds. These predictive approaches can accurately model compounds and increase true discovery rates, but they are typically black box in nature and do not generate specific chemical insights. Explainable deep learning aims to 'open up' the black box by providing generalizable and human-understandable reasoning for model predictions. These explanations can augment molecular discovery by identifying structural classes of compounds with desired activity in lieu of lone compounds. Additionally, these explanations can guide hypothesis generation and make searching large chemical spaces more efficient. Here we present an explainable deep learning platform that enables vast chemical spaces to be mined and the chemical substructures underlying predicted activity to be identified. The platform relies on Chemprop, a software package implementing graph neural networks as a deep learning model architecture. In contrast to similar approaches, graph neural networks have been shown to be state of the art for molecular property prediction. Focusing on discovering structural classes of antibiotics, this protocol provides guidelines for experimental data generation, model implementation and model explainability and evaluation. This protocol does not require coding proficiency or specialized hardware, and it can be executed in as little as 1-2 weeks, starting from data generation and ending in the testing of model predictions. The platform can be broadly applied to discover structural classes of other small molecules, including anticancer, antiviral and senolytic drugs, as well as to discover structural classes of inorganic molecules with desired physical and chemical properties.

Frog-derived synthetic peptides display anti-infective activity against Gram-negative pathogens

Novel antibiotics are urgently needed since bacteria are becoming increasingly resistant to existing antimicrobial drugs. Furthermore, available antibiotics are broad spectrum, often causing off-target effects on host cells and the beneficial microbiome. To overcome these limitations, we used structure-guided design to generate synthetic peptides derived from Andersonin-D1, an antimicrobial peptide (AMP) produced by the odorous frog Odorrana andersonii. We found that both hydrophobicity and net charge were critical for its bioactivity, enabling the design of novel, optimized synthetic peptides. These peptides selectively targeted Gram-negative pathogens in single cultures and complex microbial consortia, showed no off-target effects on human cells or beneficial gut microbes, and did not select for bacterial resistance. Notably, they also exhibited in vivo activity in two preclinical murine models. Overall, we present synthetic peptides that selectively target pathogenic infections and offer promising preclinical antibiotic candidates.

Integrating DNA and RNA sequencing for enhanced pathogen detection in respiratory infections

BACKGROUND

The clinical value of shotgun metagenomic next-generation sequencing (mNGS) in improving the detection rates of respiratory pathogens is well-established. However, mNGS is complex and expensive. This study designed and evaluated the performance of targeted NGS (tNGS) in diagnosing respiratory infections.

METHODS

We retrospectively included samples from 281 patients with lower respiratory tract infections to establish thresholds of pathogens. Subsequently, target pathogens were selected and a probe hybridization system was established. The performance and clinical manifestations of tNGS for 306 pathogens were evaluated using clinical and simulated samples.

RESULTS

The tNGS method took 16 h with sequencing data sizes of 5 M reads. The limit-of-detection of tNGS was 100-200 CFU/mL, respectively. Bioinformatics simulation confirmed the method's high specificity and robustness. In 281 patients of clinical validation cohort, tNGS exhibited a sensitivity of 97.73% and specificity of 75.41% compared to the composite reference standard, which notably surpasses those of culture-based and conventional microbiological methods (CMT). In detecting bacterial and viral infection, tNGS demonstrated superior sensitivity relative to CMT. Notably, 61.40% of target viruses were subtype-resolved with the initial establishment of reliable typing cutoffs, with the subtyping results being completely consistent with the PCR results. tNGS allowed for concurrent identification of antimicrobial resistance (AMR) markers and viral subtyping. 80.56% of AMR markers identified by tNGS were consistent with antimicrobial susceptibility testing.

CONCLUSION

This research established the robust performance of our tailored tNGS assay in the simultaneous detection of DNA and RNA pathogens, underscoring its prospective suitability for widespread use in clinical diagnostics.

Landscape of small nucleic acid therapeutics: moving from the bench to the clinic as next-generation medicines

The ability of small nucleic acids to modulate gene expression via a range of processes has been widely explored. Compared with conventional treatments, small nucleic acid therapeutics have the potential to achieve long-lasting or even curative effects via gene editing. As a result of recent technological advances, efficient small nucleic acid delivery for therapeutic and biomedical applications has been achieved, accelerating their clinical translation. Here, we review the increasing number of small nucleic acid therapeutic classes and the most common chemical modifications and delivery platforms. We also discuss the key advances in the design, development and therapeutic application of each delivery platform. Furthermore, this review presents comprehensive profiles of currently approved small nucleic acid drugs, including 11 antisense oligonucleotides (ASOs), 2 aptamers and 6 siRNA drugs, summarizing their modifications, disease-specific mechanisms of action and delivery strategies. Other candidates whose clinical trial status has been recorded and updated are also discussed. We also consider strategic issues such as important safety considerations, novel vectors and hurdles for translating academic breakthroughs to the clinic. Small nucleic acid therapeutics have produced favorable results in clinical trials and have the potential to address previously "undruggable" targets, suggesting that they could be useful for guiding the development of additional clinical candidates.

A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis

This paper explores the transformative potential of integrating artificial intelligence (AI) in the diagnosis and prognosis of infectious diseases. By analyzing diverse datasets, including clinical symptoms, laboratory results, and imaging data, AI algorithms can significantly enhance early detection and personalized treatment strategies. This paper reviews how AI-driven models improve diagnostic accuracy, predict patient outcomes, and contribute to effective disease management. It also addresses the challenges and ethical considerations associated with AI, including data privacy, algorithmic bias, and equitable access to healthcare. Highlighting case studies and recent advancements, the paper underscores AI's role in revolutionizing infectious disease management and its implications for future healthcare delivery.

Epitope-Based Vaccines: The Next Generation of Promising Vaccines Against Bacterial Infection

The increasing resistance of bacteria to antibiotics has underscored the need for new drugs or vaccines to prevent bacterial infections. Reducing multidrug resistance is a key objective of the WHO's One Health initiative. Epitopes, the key parts of antigen molecules that determine their specificity, directly stimulate the body to produce specific humoral and/or cellular immune responses. Epitope-based vaccines, which combine dominant epitopes in a rational manner, induce a more efficient and specific immune response than the original antigen. While these vaccines face significant challenges, such as epitope escape or low immunogenicity, they offer advantages including minimal adverse reactions, improved efficacy, and optimized protection. As a result, epitope-based vaccines are considered a promising next-generation approach to combating bacterial infections. This review summarizes the latest advancements, challenges, and future prospects of epitope-based vaccines targeting bacteria, with a focus on their development workflow and application in antibiotic-resistant pathogens with high mortality rates, including Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa. The goal of this review is to provide insights into next-generation vaccination strategies to combat bacterial infections associated with antibiotic resistance and high mortality rates.

Paving the way for new antimicrobial peptides through molecular de-extinction

Molecular de-extinction has emerged as a novel strategy for studying biological molecules throughout evolutionary history. Among the myriad possibilities offered by ancient genomes and proteomes, antimicrobial peptides (AMPs) stand out as particularly promising alternatives to traditional antibiotics. Various strategies, including software tools and advanced deep learning models, have been used to mine these host defense peptides. For example, computational analysis of disulfide bond patterns has led to the identification of six previously uncharacterized β-defensins in extinct and critically endangered species. Additionally, artificial intelligence and machine learning have been utilized to uncover ancient antibiotics, revealing numerous candidates, including mammuthusin, and elephasin, which display inhibitory effects toward pathogens in vitro and in vivo. These innovations promise to discover novel antibiotics and deepen our insight into evolutionary processes.

Generative latent diffusion language modeling yields anti-infective synthetic peptides

Generative artificial intelligence (AI) offers a powerful avenue for peptide design, yet this process remains challenging due to the vast sequence space, complex structure-activity relationships, and the need to balance antimicrobial potency with low toxicity. Traditional approaches often rely on trial-and-error screening and fail to efficiently navigate the immense diversity of potential sequences. Here, we introduce AMP-Diffusion, a novel latent diffusion model fine-tuned on antimicrobial peptide (AMP) sequences using embeddings from protein language models. By systematically exploring sequence space, AMP-Diffusion enables the rapid discovery of promising antibiotic candidates. We generated 50,000 candidate sequences, which were subsequently filtered and ranked using our APEX predictor model. From these, 46 top candidates were synthesized and experimentally validated. The resulting AMP-Diffusion peptides demonstrated broad-spectrum antibacterial activity, targeting clinically relevant pathogens-including multidrug-resistant strains-while exhibiting low cytotoxicity in human cell assays. Mechanistic studies revealed bacterial killing via membrane permeabilization and depolarization, and the peptides showed favorable physicochemical profiles. In preclinical mouse models of infection, lead peptides effectively reduced bacterial burdens, displaying efficacy comparable to polymyxin B and levofloxacin, with no detectable adverse effects. This study highlights the potential of AMP-Diffusion as a robust generative platform for designing novel antibiotics and bioactive peptides, offering a promising strategy to address the escalating challenge of antimicrobial resistance.

Metabolic engineering approaches for the biosynthesis of antibiotics

BACKGROUND

Antibiotics have been saving countless lives from deadly infectious diseases, which we now often take for granted. However, we are currently witnessing a significant rise in the emergence of multidrug-resistant (MDR) bacteria, making these infections increasingly difficult to treat in hospitals.

MAIN TEXT

The discovery and development of new antibiotic has slowed, largely due to reduced profitability, as antibiotics often lose effectiveness quickly as pathogenic bacteria evolve into MDR strains. To address this challenge, metabolic engineering has recently become crucial in developing efficient enzymes and cell factories capable of producing both existing antibiotics and a wide range of new derivatives and analogs. In this paper, we review recent tools and strategies in metabolic engineering and synthetic biology for antibiotic discovery and the efficient production of antibiotics, their derivatives, and analogs, along with representative examples.

CONCLUSION

These metabolic engineering and synthetic biology strategies offer promising potential to revitalize the discovery and development of new antibiotics, providing renewed hope in humanity's fight against MDR pathogenic bacteria.

Leveraging large language models for peptide antibiotic design

Large language models (LLMs) have significantly impacted various domains of our society, including recent applications in complex fields such as biology and chemistry. These models, built on sophisticated neural network architectures and trained on extensive datasets, are powerful tools for designing, optimizing, and generating molecules. This review explores the role of LLMs in discovering and designing antibiotics, focusing on peptide molecules. We highlight advancements in drug design and outline the challenges of applying LLMs in these areas.

Improving functional protein generation via foundation model-derived latent space likelihood optimization

A variety of deep generative models have been adopted to perform de novo functional protein generation. Compared to 3D protein design, sequence-based generation methods, which aim to generate amino acid sequences with desired functions, remain a major approach for functional protein generation due to the abundance and quality of protein sequence data, as well as the relatively low modeling complexity for training. Although these models are typically trained to match protein sequences from the training data, exact matching of every amino acid is not always essential. Certain amino acid changes (e.g., mismatches, insertions, and deletions) may not necessarily lead to functional changes. This suggests that maximizing the training data likelihood beyond the amino acid sequence space could yield better generative models. Pre-trained protein large language models (PLMs) like ESM2 can encode protein sequences into a latent space, potentially serving as functional validators. We propose training functional protein sequence generative models by simultaneously optimizing the likelihood of training data in both the amino acid sequence space and the latent space derived from a PLM. This training scheme can also be viewed as a knowledge distillation approach that dynamically re-weights samples during training. We applied our method to train GPT-like models (i.e., autoregressive transformers) for antimicrobial peptide (AMP) and malate dehydrogenase (MDH) generation tasks. Computational experiments confirmed that our method outperformed various deep generative models (e.g., generative adversarial net, variational autoencoder, and GPT model without the proposed training strategy) on these tasks, demonstrating the effectiveness of our multi-likelihood optimization strategy.

Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance

Artificial intelligence (AI) has transformed infectious disease control, enhancing rapid diagnosis and antibiotic discovery. While conventional tests delay diagnosis, AI-driven methods like machine learning and deep learning assist in pathogen detection, resistance prediction, and drug discovery. These tools improve antibiotic stewardship and identify effective compounds such as antimicrobial peptides and small molecules. This review explores AI applications in diagnostics, therapy, and drug discovery, emphasizing both strengths and areas needing improvement.

Development of the design and synthesis of metal-organic frameworks (MOFs) - from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions

Owing to the exceptional porous properties of metal-organic frameworks (MOFs), there has recently been a surge of interest, evidenced by a plethora of research into their design, synthesis, properties, and applications. This expanding research landscape has driven significant advancements in the precise regulation of MOF design and synthesis. Initially dominated by large-scale synthesis approaches, this field has evolved towards more targeted functional modifications. Recently, the integration of computational science, particularly through artificial intelligence predictions, has ushered in a new era of innovation, enabling more precise and efficient MOF design and synthesis methodologies. The objective of this review is to provide readers with an extensive overview of the development process of MOF design and synthesis, and to present visions for future developments.

Role of artificial intelligence in early diagnosis and treatment of infectious diseases

Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering promising solutions to address this challenge. This review article provides a comprehensive overview of the pivotal role AI can play in the early diagnosis and treatment of infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance. Furthermore, it delves into the potential of AI to predict disease outbreaks, optimise treatment strategies, and personalise interventions based on individual patient data and how AI can be used to gear up the drug discovery and development (D3) process.The ethical considerations, challenges, and limitations associated with the integration of AI in infectious disease management are also examined. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases.

Programming mammalian cell behaviors by physical cues

In recent decades, the field of synthetic biology has witnessed remarkable progress, driving advances in both research and practical applications. One pivotal area of development involves the design of transgene switches capable of precisely regulating specified outputs and controlling cell behaviors in response to physical cues, which encompass light, magnetic fields, temperature, mechanical forces, ultrasound, and electricity. In this review, we delve into the cutting-edge progress made in the field of physically controlled protein expression in engineered mammalian cells, exploring the diverse genetic tools and synthetic strategies available for engineering targeting cells to sense these physical cues and generate the desired outputs accordingly. We discuss the precision and efficiency limitations inherent in these tools, while also highlighting their immense potential for therapeutic applications.

Medicine Meets Science: The Imperative of Scientific Research and Publishing for Physician-Scientists

Physician-scientists serve as conduits between clinical practice and scientific research, leveraging their unique expertise to improve patient care and drive medical innovation. This article highlights the indispensable role of research and publishing in promoting evidence-based practices, facilitating professional growth, and shaping public health policy. Drawing on historical and contemporary examples, I examine the challenges faced by physician-scientists, such as ethical dilemmas and declining engagement in research, particularly in resource-constrained settings. I suggest pragmatic strategies to overcome these barriers, emphasizing the need for systemic support, ethical integrity, and the equitable dissemination of advancements. This piece aims to inspire a new generation of physician-scientists to engage deeply with both clinical and research domains, thus advancing global health equity and resilience.

AI Methods for Antimicrobial Peptides: Progress and Challenges

Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models. Initially, classical ML approaches dominated the field, but recently there has been a shift towards deep learning (DL) models. Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure-guided AMP discovery and design. This review aims to fill that gap by providing a comprehensive overview of the latest advancements, challenges and opportunities in using AI methods, with a particular emphasis on LLMs, GNNs and structure-guided design. We discuss the limitations of current approaches and highlight the most relevant topics to address in the coming years for AMP discovery and design.

Peptides from non-immune proteins target infections through antimicrobial and immunomodulatory properties

Encrypted peptides (EPs) have been recently described as a new class of antimicrobial molecules. They have been found in numerous organisms and have been proposed to have a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these EPs are found embedded in proteins unrelated to the immune system, suggesting that immunological responses extend beyond traditional host immunity proteins. To test this idea, we synthesized and analyzed representative peptides derived from non-immune human proteins for their ability to exert antimicrobial and immunomodulatory properties. Most of the tested peptides from non-immune proteins, derived from structural proteins as well as proteins from the nervous and visual systems, displayed potent in vitro antimicrobial activity. These molecules killed bacterial pathogens by targeting their membrane, and those originating from the same region of the body exhibited synergistic effects when combined. Beyond their antimicrobial properties, nearly 90% of the peptides tested exhibited immunomodulatory effects, modulating inflammatory mediators, such as interleukin (IL)-6, tumor necrosis factor (TNF)-α, and monocyte chemoattractant protein-1 (MCP-1). Moreover, eight of the peptides identified, collagenin-3 and 4, zipperin-1 and 2, and immunosin-2, 3, 12, and 13, displayed anti-infective efficacy in two different preclinical mouse models, reducing bacterial infections by up to four orders of magnitude. Altogether, our results support the hypothesis that peptides from non-immune proteins may have a role in host immunity. These results potentially expand our notion of the immune system to include previously unrecognized proteins and peptides that may be activated upon infection to confer protection to the host.

Antimicrobial resistance: a concise update

Antimicrobial resistance (AMR) is a serious threat to global public health, with approximately 5 million deaths associated with bacterial AMR in 2019. Tackling AMR requires a multifaceted and cohesive approach that ranges from increased understanding of mechanisms and drivers at the individual and population levels, AMR surveillance, antimicrobial stewardship, improved infection prevention and control measures, and strengthened global policies and funding to development of novel antimicrobial therapeutic strategies. In this rapidly advancing field, this Review provides a concise update on AMR, encompassing epidemiology, evolution, underlying mechanisms (primarily those related to last-line or newer generation of antibiotics), infection prevention and control measures, access to antibiotics, antimicrobial stewardship, AMR surveillance, and emerging non-antibiotic therapeutic approaches. The Review also discusses the potential roles of artificial intelligence in addressing AMR, including antimicrobial susceptibility testing, AMR surveillance, antimicrobial stewardship, diagnosis, and antimicrobial drug discovery and development. This Review highlights the urgent need for addressing the global effects of AMR and for rapid advancement of relevant technology in this dynamic field.

Computational Approaches for Antimicrobial Peptide Delivery

Peptides constitute alternative molecules for the treatment of infections caused by bacteria, viruses, fungi, and protozoa. However, their therapeutic effectiveness is often limited by enzymatic degradation, chemical and physical instability, and toxicity toward healthy human cells. To improve their pharmacokinetic (PK) and pharmacodynamic (PD) profiles, novel routes of administration are being explored. Among these, nanoparticles have shown promise as potential carriers for peptides, although the design of delivery vehicles remains a slow and painstaking process, heavily reliant on trial and error. Recently, computational approaches have been introduced to accelerate the development of effective drug delivery systems for peptides. Here we present an overview of some of these computational strategies and discuss their potential to optimize drug development and delivery.

Designing nanotheranostics with machine learning

The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as 'nanotheranostics'. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano-bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper

Antibiotic resistance is a global public health concern. Bacteria have evolved resistance to most antibiotics, which means that for any given bacterial infection, the bacteria may be resistant to one or several antibiotics. It has been suggested that genomic sequencing and machine learning (ML) could make resistance testing more accurate and cost-effective. Given that ML is likely to become an ever more important tool in medicine, we believe that it is important for pre-health students and others in the life sciences to learn to use ML tools. This paper provides a step-by-step tutorial to train 4 different ML models (logistic regression, random forests, extreme gradient-boosted trees, and neural networks) to predict drug resistance for Escherichia coli isolates and to evaluate their performance using different metrics and cross-validation techniques. We also guide the user in how to load and prepare the data used for the ML models. The tutorial is accessible to beginners and does not require any software to be installed as it is based on Google Colab notebooks and provides a basic understanding of the different ML models. The tutorial can be used in undergraduate and graduate classes for students in Biology, Public Health, Computer Science, or related fields.

Artificial intelligence in nanotechnology for treatment of diseases

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

[The future of medicine: an informed look into the "crystal ball"]

This article explores potential future scenarios for the medical field based on current trends, technological advancements, and social dynamics. By examining advances in artificial intelligence, immersive technologies, genomics, and digital health infrastructure, the paper envisions a healthcare system poised for transformative change. The anticipated role of AI as a digital assistant in diagnostics, resource management, and personalized medicine is highlighted, alongside the implications for clinical workflows. Immersive technologies, such as VR and AR, promise enhancements in medical education, patient care, and therapeutic interventions. Advances in genomics and gene editing technologies such as CRISPR further open possibilities for personalized treatment regimens and potential cures for genetic diseases. However, these innovations introduce new ethical challenges around privacy, data security, and clinical accountability. The article also addresses the healthcare implications of climate change, aging populations, and global conflicts, urging preparedness and resilience within healthcare systems. Taken together, it emphasizes the need for a balanced approach to innovation, integrating ethical considerations to foster a future where medicine remains empathetic and human-centered, even as it becomes more data-driven and technologically complex.

A generative artificial intelligence approach for antibiotic optimization

Antimicrobial resistance (AMR) poses a critical global health threat, underscoring the urgent need for innovative antibiotic discovery strategies. While recent advances in peptide design have yielded numerous antimicrobial agents, optimizing these molecules experimentally remains challenging due to unpredictable and resource-intensive trial-and-error approaches. Here, we introduce APEX Generative Optimization (APEX GO ), a generative artificial intelligence (AI) framework that integrates a transformer-based variational autoencoder with Bayesian optimization to design and optimize antimicrobial peptides. Unlike traditional supervised learning approaches that screen fixed databases of existing molecules, APEX GO generates entirely novel peptide sequences through arbitrary modifications of template peptides, representing a paradigm shift in peptide design and antibiotic discovery. Our framework introduces a new peptide variational autoencoder with design and diversity constraints to maintain similarity to specific templates while enabling sequence innovation. This work represents the first in vitro and in vivo experimental validation of generative Bayesian optimization in any setting. Using ten de-extinct peptides as templates, APEX GO generated optimized derivatives with enhanced antimicrobial properties. We synthesized 100 of these optimized peptides and conducted comprehensive in vitro characterizations, including assessments of antimicrobial activity, mechanism of action, secondary structure, and cytotoxicity. Notably, APEX GO achieved an outstanding 85% ground-truth experimental hit rate and a 72% success rate in enhancing antimicrobial activity against clinically relevant Gram-negative pathogens, outperforming previously reported methods for antibiotic discovery and optimization. In preclinical mouse models of Acinetobacter baumannii infection, several AI-optimized molecules-most notably derivatives of mammuthusin-3 and mylodonin-2-exhibited potent anti-infective activity comparable to or exceeding that of polymyxin B, a widely used last-resort antibiotic. These findings highlight the potential of APEX GO as a novel generative AI approach for peptide design and antibiotic optimization, offering a powerful tool to accelerate antibiotic discovery and address the escalating challenge of AMR.

Advances in methods and concepts provide new insight into antibiotic fluxes across the bacterial membrane

The sophisticated envelope of Gram-negative bacteria modulates the uptake of small molecules in a side-chain-sensitive manner. Despite intensive theoretical and experimental investigations, a general set of pathways underpinning antibiotic uptake has not been identified. This manuscript discusses the passive influx versus active efflux of antibiotics, considering the responsible membrane proteins and the transported molecules. Recent methods have analyzed drug transport across the bacterial membrane in order to understand their activity. The combination of in vitro, in cellulo and in silico methods shed light on the key, mainly electrostatic, interactions between the molecule surface, porins and transporters during permeation. A key factor is the relationship between the dose of an active compound near its target and its antibacterial activity during the critical early window. Today, methodology breakthroughs provide fruitful tools to precisely dissect drug transport, identify key steps in drug resistance associated with membrane impermeability and efflux, and highlight key parameters to generate more effective drugs.

Mining biology for antibiotic discovery

The rise of antibiotic resistance calls for innovative solutions. The realization that biology can be mined digitally using artificial intelligence has revealed a new paradigm for antibiotic discovery, offering hope in the fight against superbugs.

Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis

Background

Infectious keratitis (IK) is the leading cause of corneal blindness globally. Deep learning (DL) is an emerging tool for medical diagnosis, though its value in IK is unclear. We aimed to assess the diagnostic accuracy of DL for IK and its comparative accuracy with ophthalmologists.

Methods

In this systematic review and meta-analysis, we searched EMBASE, MEDLINE, and clinical registries for studies related to DL for IK published between 1974 and July 16, 2024. We performed meta-analyses using bivariate models to estimate summary sensitivities and specificities. This systematic review was registered with PROSPERO (CRD42022348596).

Findings

Of 963 studies identified, 35 studies (136,401 corneal images from >56,011 patients) were included. Most studies had low risk of bias (68.6%) and low applicability concern (91.4%) in all domains of QUADAS-2, except the index test domain. Against the reference standard of expert consensus and/or microbiological results (seven external validation studies; 10,675 images), the summary estimates (95% CI) for sensitivity and specificity of DL for IK were 86.2% (71.6-93.9) and 96.3% (91.5-98.5). From 28 internal validation studies (16,059 images), summary estimates for sensitivity and specificity were 91.6% (86.8-94.8) and 90.7% (84.8-94.5). Based on seven studies (4007 images), DL and ophthalmologists had comparable summary sensitivity [89.2% (82.2-93.6) versus 82.2% (71.5-89.5); P = 0.20] and specificity [(93.2% (85.5-97.0) versus 89.6% (78.8-95.2); P = 0.45].

Interpretation

DL models may have good diagnostic accuracy for IK and comparable performance to ophthalmologists. These findings should be interpreted with caution due to the image-based analysis that did not account for potential correlation within individuals, relatively homogeneous population studies, lack of pre-specification of DL thresholds, and limited external validation. Future studies should improve their reporting, data diversity, external validation, transparency, and explainability to increase the reliability and generalisability of DL models for clinical deployment.

Funding

NIH, Wellcome Trust, MRC, Fight for Sight, BHP, and ESCRS.

Data-centric challenges with the application and adoption of artificial intelligence for drug discovery

INTRODUCTION

Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting the impact and scope of AI models.

AREAS COVERED

In this perspective, the authors discuss a range of data issues (bias, inconsistency, skewness, irrelevance, small size, high dimensionality), how they challenge AI models, and which issue-specific mitigations have been effective. Next, they point out the challenges faced by uncertainty quantification techniques aimed at enhancing and trusting the predictions from these AI models. They also discuss how conceptual errors, unrealistic benchmarks and performance misestimation can confound the evaluation of models and thus their development. Lastly, the authors explain how human bias, whether from AI experts or drug discovery experts, constitutes another challenge that can be alleviated by gaining more prospective experience.

EXPERT OPINION

AI models are often developed to excel on retrospective benchmarks unlikely to anticipate their prospective performance. As a result, only a few of these models are ever reported to have prospective value (e.g. by discovering potent and innovative drug leads for a therapeutic target). The authors have discussed what can go wrong in practice with AI for drug discovery. The authors hope that this will help inform the decisions of editors, funders investors, and researchers working in this area.

Ultrasound-Mediated Antibiotic Delivery to In Vivo Biofilm Infections: A Review

Bacterial biofilms are a significant concern in various medical contexts due to their resilience to our immune system as well as antibiotic therapy. Biofilms often require surgical removal and frequently lead to recurrent or chronic infections. Therefore, there is an urgent need for improved strategies to treat biofilm infections. Ultrasound-mediated drug delivery is a technique that combines ultrasound application, often with the administration of acoustically-active agents, to enhance drug delivery to specific target tissues or cells within the body. This method involves using ultrasound waves to assist in the transportation or activation of medications, improving their penetration, distribution, and efficacy at the desired site. The advantages of ultrasound-mediated drug delivery include targeted and localized delivery, reduced systemic side effects, and improved efficacy of the drug at lower doses. This review scrutinizes recent advances in the application of ultrasound-mediated drug delivery for treating biofilm infections, focusing on in vivo studies. We examine the strengths and limitations of this technology in the context of wound infections, device-associated infections, lung infections and abscesses, and discuss current gaps in knowledge and clinical translation considerations.

The transmission dynamics of an infectious disease model in fractional derivative with vaccination under real data

The resurgence of monkeypox causes considerable healthcare risks needing efficient immunization programs. This work investigates the monkeypox disease dynamics in the UK, focusing on the impact of vaccination under real data. The key difficulty is to correctly predict the spread of the disease and evaluate the success of immunization efforts. We construct a mathematical model for monkeypox infection and extend it to the fractional case considering the Caputo derivative. The analysis ensures the positivity, boundedness, and uniqueness of the solution for the non-integer system. We conduct a local asymptotical stability analysis (LAS) at the disease-free equilibrium (DFE) D0, showing the result for R0<1. Additionally, we demonstrate the existence of multiple endemic equilibria and provide conditions for backward bifurcation, which are illustrated graphically. Using real case data from the UK, we estimate model parameters via the nonlinear least square method. Our results show that, without vaccination, R2≈0.8, whereas vaccination reduces it to R2v=0.48. We perform sensitivity analysis to identify key parameters influencing disease elimination, presenting the outcomes through graphs. To solve numerically the fractional model, we outline a numerical scheme and provide detailed results under various parameter assumptions. Our findings suggest that high vaccine efficacy, a low waning rate of the vaccines, and increased vaccination of the infected people can significantly reduce the future cases of monkeypox in the UK. The present study offers a comprehensive framework for monkeypox dynamics and informs public health strategies for effective disease control and prevention.

Artificial intelligence and prescription of antibiotic therapy: present and future

INTRODUCTION

In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription.

AREAS COVERED

In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024.

EXPERT OPINION

Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.

Mining human microbiomes reveals an untapped source of peptide antibiotics

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally screened 444,054 previously reported putative small protein families from 1,773 human metagenomes for antimicrobial properties, identifying 323 candidates encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 70.5% displaying antimicrobial activity. As these compounds were different compared with previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergizing with each other, and modulating gut commensals, indicating a potential role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. Our report supports the existence of hundreds of antimicrobials in the human microbiome amenable to clinical translation.

Emerging and reemerging infectious diseases: global trends and new strategies for their prevention and control

To adequately prepare for potential hazards caused by emerging and reemerging infectious diseases, the WHO has issued a list of high-priority pathogens that are likely to cause future outbreaks and for which research and development (R&D) efforts are dedicated, known as paramount R&D blueprints. Within R&D efforts, the goal is to obtain effective prophylactic and therapeutic approaches, which depends on a comprehensive knowledge of the etiology, epidemiology, and pathogenesis of these diseases. In this process, the accessibility of animal models is a priority bottleneck because it plays a key role in bridging the gap between in-depth understanding and control efforts for infectious diseases. Here, we reviewed preclinical animal models for high priority disease in terms of their ability to simulate human infections, including both natural susceptibility models, artificially engineered models, and surrogate models. In addition, we have thoroughly reviewed the current landscape of vaccines, antibodies, and small molecule drugs, particularly hopeful candidates in the advanced stages of these infectious diseases. More importantly, focusing on global trends and novel technologies, several aspects of the prevention and control of infectious disease were discussed in detail, including but not limited to gaps in currently available animal models and medical responses, better immune correlates of protection established in animal models and humans, further understanding of disease mechanisms, and the role of artificial intelligence in guiding or supplementing the development of animal models, vaccines, and drugs. Overall, this review described pioneering approaches and sophisticated techniques involved in the study of the epidemiology, pathogenesis, prevention, and clinical theatment of WHO high-priority pathogens and proposed potential directions. Technological advances in these aspects would consolidate the line of defense, thus ensuring a timely response to WHO high priority pathogens.

Leveraging artificial intelligence in vaccine development: A narrative review

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.

Smart Nanozymes for Diagnosis of Bacterial Infection: The Next Frontier from Laboratory to Bedside Testing

The global spread of infectious diseases caused by pathogenic bacteria significantly poses public health concerns, and methods for sensitive, selective, and facile diagnosis of bacteria can efficiently prevent deterioration and further spreading of the infections. The advent of nanozymes has broadened the spectrum of alternatives for diagnosing bacterial infections. Compared to natural enzymes, nanozymes exhibit the same enzymatic characteristics but offer greater economic efficiency, enhanced durability, and adjustable dimensions. The importance of early diagnosis of bacterial infection and conventional diagnostic approaches is introduced. Subsequently, the review elucidates the definition, properties, and catalytic mechanism of nanozymes. Eventually, the detailed application of nanozymes in detecting bacteria is explored, highlighting their utilization as biosensors that allow for accelerated and highly sensitive identification of bacterial infections and reflecting on the potential of nanozyme-based bacterial detection as a point-of-care testing (POCT) tool. A brief summary of obstacles and future perspectives in this field is presented at the conclusion of this review.

Infection Prevention and Control Strategies According to the Type of Multidrug-Resistant Bacteria and Candida auris in Intensive Care Units: A Pragmatic Resume including Pathogens R0 and a Cost-Effectiveness Analysis

Multidrug-resistant organism (MDRO) outbreaks have been steadily increasing in intensive care units (ICUs). Still, healthcare institutions and workers (HCWs) have not reached unanimity on how and when to implement infection prevention and control (IPC) strategies. We aimed to provide a pragmatic physician practice-oriented resume of strategies towards different MDRO outbreaks in ICUs. We performed a narrative review on IPC in ICUs, investigating patient-to-staff ratios; education, isolation, decolonization, screening, and hygiene practices; outbreak reporting; cost-effectiveness; reproduction numbers (R0); and future perspectives. The most effective IPC strategy remains unknown. Most studies focus on a specific pathogen or disease, making the clinician lose sight of the big picture. IPC strategies have proven their cost-effectiveness regardless of typology, country, and pathogen. A standardized, universal, pragmatic protocol for HCW education should be elaborated. Likewise, the elaboration of a rapid outbreak recognition tool (i.e., an easy-to-use mathematical model) would improve early diagnosis and prevent spreading. Further studies are needed to express views in favor or against MDRO decolonization. New promising strategies are emerging and need to be tested in the field. The lack of IPC strategy application has made and still makes ICUs major MDRO reservoirs in the community. In a not-too-distant future, genetic engineering and phage therapies could represent a plot twist in MDRO IPC strategies.

Computational Design of Pore-Forming Peptides with Potent Antimicrobial and Anticancer Activities

Peptides that form transmembrane barrel-stave pores are potential alternative therapeutics for bacterial infections and cancer. However, their optimization for clinical translation is hampered by a lack of sequence-function understanding. Recently, we have de novo designed the first synthetic barrel-stave pore-forming antimicrobial peptide with an identified function of all residues. Here, we systematically mutate the peptide to improve pore-forming ability in anticipation of enhanced activity. Using computer simulations, supported by liposome leakage and atomic force microscopy experiments, we find that pore-forming ability, while critical, is not the limiting factor for improving activity in the submicromolar range. Affinity for bacterial and cancer cell membranes needs to be optimized simultaneously. Optimized peptides more effectively killed antibiotic-resistant ESKAPEE bacteria at submicromolar concentrations, showing low cytotoxicity to human cells and skin model. Peptides showed systemic anti-infective activity in a preclinical mouse model of Acinetobacter baumannii infection. We also demonstrate peptide optimization for pH-dependent antimicrobial and anticancer activity.