ArticleScientific reports2021
ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Dynamic Fourier wavelet positional encoding for anticancer peptide prediction via transfer learning.iScience · 2026Article
- Mechanism-Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Venom-Derived Enzyme Inhibitors as Anticancer Agents: Structure-Activity Relationships, Molecular Targets and Mechanistic Insights.Molecules (Basel, Switzerland) · 2026Review
- SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.Briefings in bioinformatics · 2026Article
- DeepACPred: an integrated multistage framework for anticancer peptide discovery and activity prediction.Briefings in bioinformatics · 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
- Enhancing anticancer peptide discovery: A fusion-centric framework with conditional diffusion for prediction and generation.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
- ProtT5-MSCRNet: a multi-scale convolutional and channel-recalibrated deep learning framework for anticancer peptide prediction.Frontiers in artificial intelligence · 2026Article
- AttBiLSTM_DE: enhancing anticancer peptide prediction using word embedding and an optimized attention-based BiLSTM framework.Scientific reports · 2025Article
- iALP: Identification of Allergenic Proteins Based on Large Language Model and Gate Linear Unit.Interdisciplinary sciences, computational life sciences · 2025Article
- A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery.Nature communications · 2025Article
- iACP-DPNet: a dual-pooling causal dilated convolutional network for interpretable anticancer peptide identification.Functional & integrative genomics · 2025Article
- MultiPep-DLCL: recognition of multifunctional therapeutic peptides through deep learning with label-sequence contrastive learning.Briefings in bioinformatics · 2025Article
- iMFP-LG: Identify Novel Multi-functional Peptides Using Protein Language Models and Graph-based Deep Learning.Genomics, proteomics & bioinformatics · 2025Article
- pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.Scientific reports · 2025Article
- ACP-CapsPred: an explainable computational framework for identification and functional prediction of anticancer peptides based on capsule network.Briefings in bioinformatics · 2024Article
- PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation.Scientific reports · 2024Article
- Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.Briefings in bioinformatics · 2024Review
Corrections and comments
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Authors and funding
7 authors.
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Abstract
Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification using different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin on our employed benchmarks. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN . ACP-MHCNN is also publicly available as an online predictor at: https://anticancer.pythonanywhere.com/ .
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