ArticleScientific data2024
DCTPep, the data of cancer therapy peptides.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Peptide language pragmatic analysis and two-stage hierarchical learning framework for therapeutic peptide prediction.BMC biology · 2026Article
- DeepPepQSAR: all-in-one for comprehensively exploiting the vast molecular diversity space of bioactive peptide universe.Molecular diversity · 2026Article
- Dual Activity Microbial Peptides Catalog.Scientific data · 2026Article
- HORDB 2.0: a comprehensive dataset of peptide hormones.Scientific data · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- Simulation-guided design of peptide-metal coordination interfaces for next-generation metallo-immunotherapy.Nano convergence · 2026Review
- Anticancer Bioactive Peptides from Plants: Sources, Action Mechanisms, and Potential Superiority to Conventional Chemotherapy.Probiotics and antimicrobial proteins · 2026Review
- Multi-ACPNet: A multi-scale sequence-structure feature fusion framework for anticancer peptide identification and functional prediction.PLoS computational biology · 2026Article
- Enhancing anticancer peptide discovery: A fusion-centric framework with conditional diffusion for prediction and generation.PLoS computational biology · 2026Article
- Comparative analysis of tissue-specific anticancer peptide prediction models: ACP-Boost framework.Frontiers in molecular biosciences · 2026Article
- Dynamic Visualization of Computer-Aided Peptide Design for Cancer Therapeutics.Drug design, development and therapy · 2025Review
- DCTPep, the data of cancer therapy peptides.Scientific data · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
With the discovery of the therapeutic activity of peptides, they have emerged as a promising class of anti-cancer agents due to their specific targeting, low toxicity, and potential for high selectivity. In particular, as peptide-drug conjugates enter clinical, the coupling of targeted peptides with traditional chemotherapy drugs or cytotoxic agents will become a new direction in cancer treatment. To facilitate the drug development of cancer therapy peptides, we have constructed DCTPep, a novel, open, and comprehensive database for cancer therapy peptides. In addition to traditional anticancer peptides (ACPs), the peptide library also includes peptides related to cancer therapy. These data were collected manually from published research articles, patents, and other protein or peptide databases. Data on drug library include clinically investigated and/or approved peptide drugs related to cancer therapy, which mainly come from the portal websites of drug regulatory authorities and organisations in different countries and regions. DCTPep has a total of 6214 entries, we believe that DCTPep will contribute to the design and screening of future cancer therapy peptides.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.