{
  "abstract": "Therapeutic peptides combine biological specificity with chemical versatility, and artificial intelligence (AI) now contributes to their identification, design, and optimisation. Peptide discovery, however, cannot be reduced to generating amino-acid sequences: therapeutic performance also depends on chemistry, conformation, assay context, stability, delivery, safety, manufacturability, and in vivo exposure. This critical review examines AI use across the major peptide classes: receptor agonists, protein–protein interaction inhibitors, oncology and tumour-targeting peptides, antimicrobial and phenotype-driven peptides, delivery peptides, vaccine epitopes, self-assembling materials, and chemically constrained macrocycles. It compares sequence-based, chemical, graph, structure-aware, and multimodal representations across predictive, generative, and experimentally integrated workflows. Coverage spans recent general protein foundation, prediction, and design systems (ProtGPT2, ESM-2/ESM3, ProGen/ProGen2, ProteinMPNN, RFdiffusion, and AlphaFold 3) and peptide-focused methods (RFpeptides, PepFlow, and PepGLAD). Particular attention is given to benchmark integrity, the limits of structure prediction for short and non-canonical molecules, and the gap between retrospective and prospective evidence. To make claims comparable, the review adapts established reporting, validation, and readiness concepts into a peptide-specific, two-dimensional Peptide–AI Evidence Grid that separates methodological rigour (M0–M3) from translational depth (T0–T3), applies it to representative studies, and provides a checklist extending DOME, TRIPOD + AI, and FAIR guidance with the chemistry- and assay-specific items peptide work requires.",
  "abstract_shown": true,
  "affiliations": [
    "Institut de Biotecnologia i de Biomedicina, Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Salvador Ventura",
      "orcid": "0000-0002-9652-6351"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1wp635t1bd702.bib",
  "cited_by": 0,
  "doi": "10.3390/biom16101445",
  "fulltext_url": null,
  "id": "1wp635t1bd702",
  "issns": [
    "2218-273X"
  ],
  "landing_url": "https://www.mdpi.com/2218-273X/16/10/1445",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7220379702",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/1wp635t1bd702.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:59Z",
      "native_id": "10.3390/biom16101445",
      "source": "crossref",
      "url": "https://www.mdpi.com/2218-273X/16/10/1445"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "issn:2218-273X",
      "subfield": "general"
    }
  ],
  "title": "Artificial Intelligence for Therapeutic Peptide Discovery and Development",
  "title_text": "Artificial Intelligence for Therapeutic Peptide Discovery and Development",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:59Z",
  "url": "https://research.draconitic.com/paper/1wp635t1bd702",
  "venue": "Biomolecules",
  "versions": []
}