# Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties

arXiv: https://arxiv.org/abs/2407.03380 · HTML: https://mlsanity.com/paper/2407.03380/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction](https://mlsanity.com/paper/2309.03099/) (2023)
- [Multimodal Language and Graph Learning of Adsorption Configuration in Catalysis](https://mlsanity.com/paper/2401.07408/) (2024)
- [Classifying Antimicrobial and Multifunctional Peptides with Bayesian Network Models](https://mlsanity.com/paper/1804.06327/) (2018)
- [GPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors Using Protein Language Models](https://mlsanity.com/paper/2310.19915/) (2023)

## Built on by (most cited first, 6 of 6)

- [Large Language Model Agent for Modular Task Execution in Drug Discovery](https://mlsanity.com/paper/2507.02925/) (2025)
- [SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification](https://mlsanity.com/paper/2412.16276/) (2024)
- [Polymer-Agent: Large Language Model Agent for Polymer Design](https://mlsanity.com/paper/2601.16376/) (2026)
- [Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification](https://mlsanity.com/paper/2503.19186/) (2025)
- [Pepti-Agent: An AI Agent for Peptide Design and Optimization](https://mlsanity.com/paper/2606.15422/) (2026)
- [Meta-Learning for Cross-Task Generalization in Protein Mutation Property Prediction](https://mlsanity.com/paper/2510.20943/) (2025)

## Abstract (arXiv)

Peptides are essential in biological processes and therapeutics. 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 combine PeptideBERT, a transformer model tailored for peptide property prediction, with a GNN encoder to capture both sequence-based and structural features. By employing Contrastive Language-Image Pre-training (CLIP), Multi-Peptide aligns embeddings from both modalities into a shared latent space, thereby enhancing the model's predictive accuracy. Evaluations on hemolysis and nonfouling datasets demonstrate Multi-Peptide's robustness, achieving state-of-the-art 86.185% 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.