ArticleJournal of chemical information and modeling2025
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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Who cites it
10 citing papers in PubMed.
- DeepPepQSAR: all-in-one for comprehensively exploiting the vast molecular diversity space of bioactive peptide universe.Molecular diversity · 2026Article
- Rethinking peptide developability with sequence-only models: interpretable screening of microplastic-binding peptides with gated query pooling.Chemical science · 2026Article
- Polymer-Agent: Large Language Model Agent for Polymer Design.Journal of chemical information and modeling · 2026Article
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- Large Language Model Agent for Modular Task Execution in Drug Discovery.Journal of chemical information and modeling · 2026Article
- DPAS: disease-associated peptide anomaly score for identifying pathogenic peptides via one-class learning.Scientific reports · 2026Article
- Multi-AOP: a lightweight multi-view deep learning framework for antioxidant peptide discovery.Bioresources and bioprocessing · 2026Article
- SGAC: a graph neural network framework for imbalanced and structure-aware AMP classification.Briefings in bioinformatics · 2026Article
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification.Journal of chemical theory and computation · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peptides are crucial in biological processes and therapeutic applications. Given their importance, advancing our ability to predict peptide properties is essential. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with graph neural networks (GNNs) to predict peptide properties. We integrate PeptideBERT, a transformer model specifically designed for peptide property prediction, with a GNN encoder to capture both sequence-based and structural features. By employing a contrastive loss framework, Multi-Peptide aligns embeddings from both modalities into a shared latent space, thereby enhancing the transformer model's predictive accuracy. Evaluations on hemolysis and nonfouling data sets demonstrate Multi-Peptide's robustness, achieving state-of-the-art 88.057% accuracy in hemolysis prediction. This study highlights the potential of multimodal learning in bioinformatics, paving the way for accurate and reliable predictions in peptide-based research and applications.
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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.