{
  "abstract": "Background: Public tumor-homing peptide (THP) resources have enabled predictors such as THPep, SCMTHP, StackTHPred, StackTHP and LLM4THP. At the same time, broader peptide machine-learning resources, including the recent PepBenchmark/PepBenchData initiative, are moving the field toward standardized datasets, preprocessing, and leaderboards across peptide-property tasks. These developments have improved comparability but have not removed task-specific problems in THP modelling: available THP benchmarks are motif-rich, chemically redundant, and susceptible to optimistic evaluation under near-neighbor overlap and potential leakage. A cheminformatics evaluation of THP predictions therefore needs to ask not only whether a peptide receives a high score, but also whether that score is independent, motif-driven, chemically redundant, hard-negative-sensitive, or near-duplicate-dependent.\n\nResults: We present THP-NanoTarget, a reproducible peptide-cheminformatics framework for leakage-aware THP benchmarking, motif-rule comparisons, Morgan/ECFP chemical-space auditing, hard-negative evaluations, strict external-recognition filtering, and conservative analog prioritization. The primary benchmark contained 4202 peptides: 1401 THP positives and 2801 primary negatives. Under standard stratified out-of-fold evaluations, a term frequency-inverse document frequency (TF-IDF) 2–4-mer logistic-regression model achieved an area under the receiver operating characteristic curve (AUROC) of 0.784 and an area under the precision recall curve (AUPRC) of 0.716, improving over a transparent motif-rule baseline (AUROC 0.644; AUPRC 0.488). Cluster-aware validation was more conservative, with an AUROC of 0.708 and an AUPRC of 0.597 for the reference k-mer model and an AUROC of 0.715 and AUPRC of 0.612 for the best rank-averaged ensemble. Chemical-neighbor exclusion revealed local-neighborhood dependence: the AUROC declined to 0.721 at a Tanimoto of < 0.80 and 0.623 at < 0.70, while the 0.60 threshold was not evaluable. A benchmark redundancy analysis showed a median nearest-neighbor Morgan/extended connectivity fingerprint (ECFP) Tanimoto similarity of 0.837, and 95.8% of peptides had at least one neighbor at ≥ 0.70. A strict external-recognition analysis retained only two of 29 literature-derived candidates after nearest-primary Tanimoto < 0.90 filtering. Conservative analog generation produced 9477 candidates, dominated by close analogs, near-duplicates, and duplicate-like variants of known positives.\n\nConclusions: THP-NanoTarget supports transparent computational triage and benchmark auditing, not de novo biological discovery or claims of receptor binding, tumor delivery, therapeutic efficacy, safety, pharmacokinetics, or clinical actionability.\n\nScientific contribution\n\nTHP-NanoTarget advances tumor-homing peptide modelling by reframing THP prediction as a leakage-aware benchmark-reliability problem rather than a conventional sequence-classification task. Unlike prior THP predictors that mainly report classification performance, it integrates motif-family annotation, transparent motif-rule baselines, RDKit/Morgan fingerprint similarity auditing, chemical-neighbor exclusion, hard-negative testing, strict external-recognition filtering, and conservative analog-novelty classification into a reproducible peptide-cheminformatics workflow. This enables THP candidate scores to be interpreted in terms of learned sequence signal, motif recurrence, near-neighbor recovery, hard-negative ambiguity, and public-data overlap, supporting transparent computational triage without claiming experimental validation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Ashutosh Tiwari"
    },
    {
      "name": "Dyah Ika Krisnawati"
    },
    {
      "name": "Tsai-Mu Cheng"
    },
    {
      "name": "Po-Jung Hsu"
    },
    {
      "name": "Tsung-Rong Kuo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4zferp7qgd1x0.bib",
  "cited_by": 0,
  "doi": "10.1186/s13321-026-01325-z",
  "fulltext_url": null,
  "id": "4zferp7qgd1x0",
  "issns": [
    "1758-2946"
  ],
  "landing_url": "https://link.springer.com/article/10.1186/s13321-026-01325-z",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/4zferp7qgd1x0.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:52Z",
      "native_id": "10.1186/s13321-026-01325-z",
      "source": "crossref",
      "url": "https://link.springer.com/article/10.1186/s13321-026-01325-z"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:1758-2946",
      "subfield": "theoretical"
    }
  ],
  "title": "THP-NanoTarget: leakage-aware cheminformatics for tumor-homing peptide evaluation and conservative analog prioritization",
  "title_text": "THP-NanoTarget: leakage-aware cheminformatics for tumor-homing peptide evaluation and conservative analog prioritization",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:20:50Z",
  "url": "https://research.draconitic.com/paper/4zferp7qgd1x0",
  "venue": "Journal of Cheminformatics",
  "versions": []
}