Frontiers in Drug Discovery · Published 2026-07-17 · DOI 10.3389/fddsv.2026.1820604
Ana Luisa Islas-Ávila, Julián García-Vinuesa, Nicole Soto-García, Michelle Soto-García, Bárbara Gutiérrez-Cárdenas, Alejandro Inostroza-Munoz, Mehdi D. Davari, Leandro Murgas, Roberto Uribe-Paredes, Alicia Martínez-Rebollar, Julieta Sepulveda-Yañez, David Medina-Ortiz
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.
Abstract from DOAJ. Public domain (CC0 1.0).
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Islas-Ávila, A., García-Vinuesa, J., Soto-García, N., et al. (2026). Toward explainable and adaptive artificial intelligence systems for antimicrobial peptide discovery under resistance pressure. Frontiers in Drug Discovery. https://doi.org/10.3389/fddsv.2026.1820604