EticaHub Labs computational protein-design dataset — COVID-19 — run 5a6f0732-c70a-464e-8ed0-e8eaec7a76e0

Computational dataset / research audit record, not a validated treatment. This individual run contains 9 archived candidates from 3 reported iterations. The pipeline-selected candidate is #5; its internal automated grade is verified. Proposed research question (not a demonstrated outcome): Characterize binding affinity and neutralization breadth of the designed nanobody scaffold against SARS-CoV-2 variants of concern. Evidence and limitations Computational predictions only. No experimental or clinical evidence is supplied. Scores, automated checks and resolved identifiers do not establish binding, novelty, efficacy, safety or medical benefit. The stored "verified" grade is an internal automated check, not peer review or independent scientific validation. Hypothesis, approach and success criteria below are proposed research, not experiments performed. Checkpoint versions, random seeds and complete tool inputs are not established by this archive export; exact rerun reproducibility is not claimed. Internal publication gate: target extraction not fully checked Internal publication gate: no PubMed citation resolved Internal publication gate: target has no reviewed UniProt entry Internal publication gate: unresolved target symbols: COVID, RBD, CDR3, VOC, XBB Files: evidence report, original JSON, all candidate sequences, provenance manifest, license and SHA-256 checksums. Available predicted coordinate exports are included and explicitly labelled; missing structures are not reconstructed. AI-assisted research generated by the EticaHub Labs autopilot. Deaven Mabry is credited as responsible project creator, with EticaHub Labs affiliation. Cited literature remains the work of its original authors; identifier resolution is not claim support.