ArticlePLoS computational biology2022
PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Artificial Intelligence-Driven Antimicrobial Peptide Discovery: Prediction, Generation, Mining and Optimization.Probiotics and antimicrobial proteins · 2026Review
- Peptide language pragmatic analysis and two-stage hierarchical learning framework for therapeutic peptide prediction.BMC biology · 2026Article
- SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.Briefings in bioinformatics · 2026Article
- PepPharmaHub: a cloud-based platform integrating multimodel language architectures with curated data resources for therapeutic peptide discovery.BMC biology · 2026Article
- TPpred-CMvL: prediction of multi-functional therapeutic peptide using contrast multi-view learning.BMC biology · 2025Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- pCPPs-sADNN: predicting cell-penetrating peptides using self-attention based deep neural network.Scientific reports · 2025Article
- METFAN: Multisource Enhanced Therapeutic Peptide Function Prediction via Adapter Network.ACS omega · 2025Article
- Multi-feature fusion network with marginal focal dice loss for multi-label therapeutic peptide prediction.PLoS computational biology · 2025Article
- Accurate prediction of toxicity peptide and its function using multi-view tensor learning and latent semantic learning framework.Bioinformatics (Oxford, England) · 2025Article
- Pepxml: ESM2-based extreme multilabel classification of pathogen-targeted antimicrobial peptides.Briefings in bioinformatics · 2025Article
- SeekTP: identifying multifunctional therapeutic peptides via graph attention and deep property integration.Briefings in bioinformatics · 2025Article
- AMCL: supervised contrastive learning with hard sample mining for multi-functional therapeutic peptide prediction.BMC biology · 2025Article
- Identification of Multi-functional Therapeutic Peptides Based on Prototypical Supervised Contrastive Learning.Interdisciplinary sciences, computational life sciences · 2025Article
- Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.BMC genomics · 2025Article
- MultiPep-DLCL: recognition of multifunctional therapeutic peptides through deep learning with label-sequence contrastive learning.Briefings in bioinformatics · 2025Article
- misORFPred: A Novel Method to Mine Translatable sORFs in Plant Pri-miRNAs Using Enhanced Scalable k-mer and Dynamic Ensemble Voting Strategy.Interdisciplinary sciences, computational life sciences · 2025Article
- AMHF-TP: Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features.Quantitative biology (Beijing, China) · 2025Article
- iMFP-LG: Identify Novel Multi-functional Peptides Using Protein Language Models and Graph-based Deep Learning.Genomics, proteomics & bioinformatics · 2025Article
- Exploring the Chemical Features and Biomedical Relevance of Cell-Penetrating Peptides.International journal of molecular sciences · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of therapeutic peptide is a significant step for the discovery of promising therapeutic drugs. Most of the existing studies have focused on the mono-functional therapeutic peptide prediction. However, the number of multi-functional therapeutic peptides (MFTP) is growing rapidly, which requires new computational schemes to be proposed to facilitate MFTP discovery. In this study, based on multi-head self-attention mechanism and class weight optimization algorithm, we propose a novel model called PrMFTP for MFTP prediction. PrMFTP exploits multi-scale convolutional neural network, bi-directional long short-term memory, and multi-head self-attention mechanisms to fully extract and learn informative features of peptide sequence to predict MFTP. In addition, we design a class weight optimization scheme to address the problem of label imbalanced data. Comprehensive evaluation demonstrate that PrMFTP is superior to other state-of-the-art computational methods for predicting MFTP. We provide a user-friendly web server of PrMFTP, which is available at http://bioinfo.ahu.edu.cn/PrMFTP.
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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.