Jul 2026· Frontiers in Drug Discovery· Vol 6· 0 citations· 108 references
TL;DR
A methodological framework for resistance-aware antimicrobial peptide discovery is proposed built upon three complementary principles: explainable and uncertainty-aware prediction, biologically constrained and rule-guided generative design, and iterative design–test–learn workflows capable of continuously incorporating new evidence.
Abstract
Therapeutic peptides have emerged as promising candidates for combating antimicrobial resistance, particularly against multidrug-resistant pathogens for which conventional antibiotics are becoming increasingly ineffective. Although artificial intelligence has accelerated antimicrobial peptide discovery through predictive modelling, generative design, and large-scale
in silico
screening, many current workflows remain fragmented, weakly interpretable, and only loosely connected to experimental feedback. In this Perspective, we propose a methodological framework for resistance-aware antimicrobial peptide discovery built upon three complementary principles: explainable and uncertainty-aware prediction, biologically constrained and rule-guided generative design, and iterative design–test–learn workflows capable of continuously incorporating new evidence. By organising existing methodologies into adaptive and transparent discovery systems, the proposed framework supports candidate prioritisation, optimisation, and iterative refinement under evolving resistance pressures. To demonstrate these concepts in practice, we present a workflow integrating interpretable prediction, uncertainty-aware prioritisation, rule extraction, and adaptive model updating. Collectively, these elements provide a roadmap for advancing antimicrobial peptide discovery from isolated predictive tasks toward integrated and evidence-driven discovery ecosystems.
A snapshot of AI-driven technologies for AMP design is provided and two modes of AI-driven technologies for AMP design are surveyed, one concentrated on identifying whether current data possess antimicrobial activity and the other on generating AMP candidates with potential therapeutic properties (generation-oriented).
Yong-Qiang Liu, Jie Hu, Ning Zhang et al.· Synthetic and Systems Biotec...· 0 citations
This review highlights how artificial intelligence bridges bacterial genomics and antimicrobial drug discovery and offers a strategic framework for the fast-tracked, cost-effective prioritization of therapeutic targets, making it a vital resource for tackling emerging pathogens.
Thayssa de Oliveira Teixeira, Ruana Carolina Cabral da Silva, M. Alves et al.· Journal of Computer-Aided Mo...· 1 citation
The emergence of antibiotic-resistant pathogens such as Staphylococcus aureus demands accelerated antimicrobial discovery strategies. Artificial intelligence (AI) enables large-scale inference of candidate antimicrobial peptides (AMPs), yet experimental validation remains essential to determine whether predictions translate into biological function. Genome-guided mining, rather than unconstrained or randomly generated sequence exploration, offers a biologically grounded search space derived from organisms shaped by ecological and evolutionary pressures. Here, we evaluate this principle using Malassezia furfur, a skin-associated yeast that coexists with bacterial colonizers such as S. aureus, as a genomic source for AI-prioritized antimicrobial candidates. Candidate fragments were generated from two M. furfur genomes, filtered by physicochemical properties, prioritized with deep-learning AMP predictors, synthesized, and experimentally characterized. Selected peptides underwent cross-kingdom antimicrobial screening against S. aureus, combining kinetic growth and ultrastructural assays, complemented by in silico structural prediction, lipid-membrane interaction analysis, and human keratinocyte cytotoxicity evaluation. AI-guided genomic mining enriched biologically motivated sequence space for peptides with measurable antimicrobial activity, while revealing biases and generalizability limits of AI-based AMP inference. Closing the loop between genome-derived candidate generation, AI-based inference, synthesis, and functional characterization, this study provides an experimental assessment of model-guided AMP discovery and a reproducible route from computational prediction to validated antimicrobial candidates.
S. Ojeda, P. Ávila, S. Castellanos et al.· bioRxiv· 0 citations
How recent advances in machine learning are reshaping AMP research is examined, driving a shift from large-scale discovery toward precision-guided prediction and design and emphasizing integrated generative-predictive pipelines, interpretable models, and closed-loop experimental validation as key enablers for the development of potent, selective, and clinically viable antimicrobial therapeutics.
Artificial intelligence has moved convincingly beyond proof-of-concept in AMR diagnostics and discovery, but its path to the clinic now depends less on further algorithmic refinement than on prospective validation, equitable data representation, and interpretability standards that clinicians can reasonably trust.
Background and Objectives: Antimicrobial resistance (AMR) is a major challenge, particularly in intensive care units, where broad-spectrum therapy is often initiated before microbiological confirmation. Artificial intelligence (AI) may improve AMR prediction, but its clinical value depends on integration with bioengineering-enabled digital microbiology. This narrative review examines how AI, bioengineering platforms and digital microbiology can support AMR prediction, clinical decision support and antimicrobial stewardship across the sample-to-decision pipeline. Materials and Methods: A targeted narrative review was conducted using PubMed/MEDLINE and Google Scholar. Publications from 2020 onward were prioritized, while earlier seminal studies, methodological frameworks and regulatory documents were included when relevant. Evidence was synthesized across AI-based resistance prediction, antimicrobial stewardship, digital microbiology and bioengineering technologies. Results: AI and machine-learning approaches showed promising performance in patient-level resistance prediction, pathogen-level susceptibility prediction and antimicrobial stewardship. For example, model discrimination reached an AUROC of 0.936 for carbapenem-resistant Klebsiella pneumoniae prediction, while model-guided empirical therapy in Enterobacterales bloodstream infections could have increased active beta-lactam therapy from 70% to 79%. However, most evidence remains retrospective and single-centre, with limited external or prospective validation. Conclusions: AI has considerable potential to support AMR prediction and antimicrobial stewardship, but current evidence primarily demonstrates technical feasibility rather than established clinical effectiveness. Broader implementation will require rigorous validation, integration into clinical workflows, continuous monitoring and demonstration of clinical benefit.
Oana Frandeș, Leonard Azamfirei, Oana-Elena Branea et al.· Medicina· 0 citations
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