ArticleBiology methods & protocols2021
MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities.
Article in Biology methods & protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.Briefings in bioinformatics · 2026Article
- RLAnOxPeptide: an integrated framework combining transformer and reinforcement learning for efficient antioxidant peptide prediction and innovative design.Bioinformatics (Oxford, England) · 2026Article
- Release of Bioactive Peptides from Whey Protein During In Vitro Digestion and Their Effect on CCK Secretion in Enteroendocrine Cells: An In Silico and In Vitro Approach.Molecules (Basel, Switzerland) · 2026Article
- Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation.Frontiers in molecular biosciences · 2026Review
- Discovery of peptides as key regulators of metabolic and cardiovascular crosstalk.Cell reports · 2025Review
- Article
- Computational approaches for identifying neuropeptides: A comprehensive review.Molecular therapy. Nucleic acids · 2025Review
- AMHF-TP: Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features.Quantitative biology (Beijing, China) · 2025Article
- Computational-aided drug design strategies for drug discovery and development against oral diseases.Frontiers in pharmacology · 2025Review
- Bioactive Peptides in Greek Goat Colostrum: Relevance to Human Metabolism.Foods (Basel, Switzerland) · 2024Article
- Structural and functional analyses of nematode-derived antimicrobial peptides support the occurrence of direct mechanisms of worm-microbiota interactions.Computational and structural biotechnology journal · 2024Article
- Antihypertensive and Antidiabetic Drug Candidates from Milkfish (Foods (Basel, Switzerland) · 2024Article
- Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.International journal of molecular sciences · 2024Article
- Machine learning tools for peptide bioactivity evaluation - Implications for cell culture media optimization and the broader cultivated meat industry.Current research in food science · 2024Article
- Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence.Marine drugs · 2023Review
- Multi-label classification and features investigation of antimicrobial peptides with various functional classes.iScience · 2023Article
- Identification of Antioxidative Peptides Derived fromMarine drugs · 2023Article
- Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning.Antibiotics (Basel, Switzerland) · 2022Review
- Combining mass spectrometry and machine learning to discover bioactive peptides.Nature communications · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peptide-based therapeutics are here to stay and will prosper in the future. A key step in identifying novel peptide-drugs is the determination of their bioactivities. Recent advances in peptidomics screening approaches hold promise as a strategy for identifying novel drug targets. However, these screenings typically generate an immense number of peptides and tools for ranking these peptides prior to planning functional studies are warranted. Whereas a couple of tools in the literature predict multiple classes, these are constructed using multiple binary classifiers. We here aimed to use an innovative deep learning approach to generate an improved peptide bioactivity classifier with capacity of distinguishing between multiple classes. We present MultiPep: a deep learning multi-label classifier that assigns peptides to zero or more of 20 bioactivity classes. We train and test MultiPep on data from several publically available databases. The same data are used for a hierarchical clustering, whose dendrogram shapes the architecture of MultiPep. We test a new loss function that combines a customized version of Matthews correlation coefficient with binary cross entropy (BCE), and show that this is better than using class-weighted BCE as loss function. Further, we show that MultiPep surpasses state-of-the-art peptide bioactivity classifiers and that it predicts known and novel bioactivities of FDA-approved therapeutic peptides. In conclusion, we present innovative machine learning techniques used to produce a peptide prediction tool to aid peptide-based therapy development and hypothesis generation.
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