ReviewPharmaceutics2022
Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics.
Review in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed.
- Confidence-Gated Triage: Coupling Drug-Target Affinity and ADME-T Predictions to Prioritise Compounds for Docking.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Anticancer Potential of Antimicrobial Peptides: Mechanisms, Clinical Application, Challenges, and Future Prospects.Drug development research · 2026Review
- Bilayer Permeabilization and the 'Sand-in-a-Gearbox' Mechanism for Membrane-Active Molecules: Which Is Which?Antibiotics (Basel, Switzerland) · 2026Article
- Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides.Pharmaceutics · 2026Review
- Artificial intelligence-driven discovery of bioactive peptides: Computational approaches and future perspectives.aBIOTECH · 2026Review
- Peptide-Functionalized Iron Oxide Nanoparticles for Cancer Therapy: Targeting Strategies, Mechanisms, and Translational Opportunities.Molecules (Basel, Switzerland) · 2026Review
- Artificial intelligence-driven anticancer peptide discovery.iMetaOmics · 2025Review
- A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery.Nature communications · 2025Article
- HighFold-MeD: a Rosetta distillation model to accelerate structure prediction of cyclic peptides with backbone N-methylation and D-amino acids.Journal of cheminformatics · 2025Article
- Cationic antimicrobial peptides: potential templates for anticancer agents.Frontiers in medicine · 2025Review
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets.Molecular diversity · 2024Article
- Structure-aware machine learning strategies for antimicrobial peptide discovery.Scientific reports · 2024Article
- Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Anticancer peptides as novel immunomodulatory therapeutic candidates for cancer treatment.Exploration of targeted anti-tumor therapy · 2024Review
- Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota.Microbial ecology · 2023Review
- Topoisomeric Membrane-Active Peptides: A Review of the Last Two Decades.Pharmaceutics · 2023Review
- Efficient Mining of Anticancer Peptides from Gut Metagenome.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Antimicrobial peptide moricin induces ROS mediated caspase-dependent apoptosis in human triple-negative breast cancer via suppression of notch pathway.Cancer cell international · 2023Article
- Bacterial RNA virus MS2 exposure increases the expression of cancer progression genes in the LNCaP prostate cancer cell line.Oncology letters · 2023Article
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
Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.
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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.