{
  "abstract": "UnibindMD is an integrated computational platform that turns a protein target into validated de novo peptide-binder leads by chaining five orthogonal stages: deep-learning binder design (BindCraft), independent structure prediction and confidence assessment (AlphaFold3-class / Boltz-2), binding-affinity prediction (PRODIGY), developability profiling (ADMET-AI), and all-atom molecular-dynamics validation (GROMACS/CHARMM36m). The platform targets a persistent weakness of single-method design, namely that confidence metrics such as ipTM and pLDDT are self-consistent but not independent, so a design predicted to be confident may still be physically unstable. We demonstrate UnibindMD on three Andes virus (ANDV) intervention points, a setting of urgent need given a case fatality rate approaching 40%, a 2026 MV Hondius cruise-ship outbreak (13 cases, 3 deaths), and no approved therapeutic: the Gn-Gc glycoprotein complex (viral entry), the L-protein endonuclease domain (cap-snatching; PDB 6Q99), and the host VEGFR2 kinase (vascular permeability; PDB 2XIR). Across nine BindCraft campaigns (4,475 trajectories) the platform produced 464 accepted binders (acceptance 2.3-67.6%), with top candidates reaching ipTM = 0.94 and pLDDT = 0.94. The three selected leads were predicted by PRODIGY to bind with G = -9.1 to -11.1 kcal mol-1 (Kd 8 nM-0.2 M), and all three remained stably bound over 100 ns of unrestrained molecular dynamics (receptor CRMSD [≤] 2.6 [A], retained peptide fold, [≥] 99.5% of frames preserving more than half of the initial interface contacts, and 75-82% helical content). ADMET-AI flagged the expected poor oral bioavailability of large polar peptides and no PAINS alerts. By converting single-prediction design into a cross-validated, dynamics-tested workflow, UnibindMD provides a rapid, scalable, and generalizable path from any protein target to validated peptide-binder candidates.",
  "abstract_shown": true,
  "affiliations": [
    "ShenZhen UnibindMD Computation Biotech Co., Ltd."
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "X. Wu"
    },
    {
      "name": "A. Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "C. Fu"
    },
    {
      "name": "Z. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "W. Fu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/95fa6fbzkzckj.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.20.760329",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760329v1.full",
  "id": "95fa6fbzkzckj",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760329v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|vaccines and immunoinformatics approaches"
    ]
  ],
  "openalex": "W7223315380",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760329v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/95fa6fbzkzckj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:20Z",
      "native_id": "10.64898/2026.10.20.760329",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760329v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "UnibindMD: An Integrated AI and Molecular Dynamics Platform for De Novo Peptide Binder Design",
  "title_text": "UnibindMD: An Integrated AI and Molecular Dynamics Platform for De Novo Peptide Binder Design",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:20Z",
  "url": "https://research.draconitic.com/paper/95fa6fbzkzckj",
  "venue": "bioRxiv",
  "versions": []
}