ArticleMolecules (Basel, Switzerland)2019
ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers.
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Who cites it
108 citing papers in PubMed, 213 citations in OpenAlex.
- Mechanism-Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- DeepACPred: an integrated multistage framework for anticancer peptide discovery and activity prediction.Briefings in bioinformatics · 2026Article
- PepAnno: A structure-aware deep learning framework for bioactive peptide prediction, structural visualization, and physicochemical profiling.PLoS computational biology · 2026Article
- Streptococcus-derived CpsA-CpsC fusion protein induces apoptosis in cervical cancer cells through in silico and in vitro methods.Scientific reports · 2026Article
- ACP-EPC: an interpretable deep learning framework for anticancer peptide prediction utilizing pre-trained protein language model and multi-view feature extracting strategy.Molecular diversity · 2026Article
- Multi-ACPNet: A multi-scale sequence-structure feature fusion framework for anticancer peptide identification and functional prediction.PLoS computational biology · 2026Article
- Deep capsule neural network for identifying anticancer peptides using sequence to image transformation-based local embedded features.BMC biology · 2026Article
- Aegis: a transformer-based deep learning framework for the accurate identification of anticancer peptides.BMC biology · 2026Article
- PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction.Journal of cheminformatics · 2026Article
- Advances in antimicrobial peptides: promising cancer treatments and vaccines.Frontiers in medicine · 2026Review
- ProtT5-MSCRNet: a multi-scale convolutional and channel-recalibrated deep learning framework for anticancer peptide prediction.Frontiers in artificial intelligence · 2026Article
- Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.iScience · 2025Article
- Artificial intelligence-driven anticancer peptide discovery.iMetaOmics · 2025Review
- AttBiLSTM_DE: enhancing anticancer peptide prediction using word embedding and an optimized attention-based BiLSTM framework.Scientific reports · 2025Article
- Bioactive Plant Peptides: Physicochemical Features, Structure-Function Insights and Mechanism of Action.Molecules (Basel, Switzerland) · 2025Review
- HyperACP: A cutting-edge hybrid framework for anticancer peptide classification via scalable feature extraction and adaptive neighbor-based synthesis.PLoS computational biology · 2025Article
- Advancing the Accuracy of Anti-MRSA Peptide Prediction Through Integrating Multi-Source Protein Language Models.Interdisciplinary sciences, computational life sciences · 2025Article
- GRU4ACE: Enhancing ACE inhibitory peptide prediction by integrating gated recurrent unit with multi-source feature embeddings.Protein science : a publication of the Protein Society · 2025Article
- Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners.Applied biochemistry and biotechnology · 2025Review
48 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.
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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.