# M2oE: Multimodal Collaborative Expert Peptide Model

arXiv: https://arxiv.org/abs/2411.15208 · HTML: https://mlsanity.com/paper/2411.15208/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text](https://mlsanity.com/paper/2308.06911/) (2023)
- [Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling](https://mlsanity.com/paper/2304.02806/) (2023)
- [Task-Customized Mixture of Adapters for General Image Fusion](https://mlsanity.com/paper/2403.12494/) (2024)
- [Mixture of Experts for Biomedical Question Answering](https://mlsanity.com/paper/2204.07469/) (2022)
- [Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding](https://mlsanity.com/paper/2307.09169/) (2023)
- [Co-modeling the Sequential and Graphical Routes for Peptide Representation Learning](https://mlsanity.com/paper/2310.02964/) (2023)

## Abstract (arXiv)

Peptides are biomolecules comprised of amino acids that play an important role in our body. In recent years, peptides have received extensive attention in drug design and synthesis, and peptide prediction tasks help us better search for functional peptides. Typically, we use the primary sequence and structural information of peptides for model encoding. However, recent studies have focused more on single-modal information (structure or sequence) for prediction without multi-modal approaches. We found that single-modal models are not good at handling datasets with less information in that particular modality. Therefore, this paper proposes the M2oE multi-modal collaborative expert peptide model. Based on previous work, by integrating sequence and spatial structural information, employing expert model and Cross-Attention Mechanism, the model's capabilities are balanced and improved. Experimental results indicate that the M2oE model performs excellently in complex task predictions.