ArticleACS omega2021
CpACpP:
Article in ACS omega, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 37 citations in OpenAlex.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Mechanism-Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Next-Generation Anticancer Peptides: Engineering, Nanotheranostics and Clinical Translation.Nanotheranostics · 2026Review
- Animal Venoms as Peptide Libraries for the Discovery of Antiglioblastoma Agents.Biochemistry research international · 2026Review
- Dynamic Visualization of Computer-Aided Peptide Design for Cancer Therapeutics.Drug design, development and therapy · 2025Review
- MuCoCP: a priori chemical knowledge-based multimodal contrastive learning pre-trained neural network for the prediction of cyclic peptide membrane penetration ability.Bioinformatics (Oxford, England) · 2024Article
- LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets.Molecular diversity · 2024Article
- Machine learning for antimicrobial peptide identification and design.Nature reviews bioengineering · 2024Article
- Peptidome Profiling ofAntibiotics (Basel, Switzerland) · 2024Article
- Article
- TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides.Patterns (New York, N.Y.) · 2023Article
- Role of Anti-Cancer Peptides as Immunomodulatory Agents: Potential and Design Strategy.Pharmaceutics · 2022Review
- In Silico Prediction of Anti-Infective and Cell-Penetrating Peptides fromPharmaceuticals (Basel, Switzerland) · 2022Article
- The role of cell-penetrating peptides in potential anti-cancer therapy.Clinical and translational medicine · 2022Review
- Approaches for evaluation of novel CPP-based cargo delivery systems.Frontiers in pharmacology · 2022Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP (http://cbb1.ut.ac.ir/CpACpP/Index).
Identifiers
What OpenQuestion holds
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.