.png){kind=icon}
.png){kind=icon}
Return
Multi-view framework for multi-label bioactive peptide classification based on multi-modal representation learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2025.113007.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The diversity and specific biological functions of bioactive peptides make them key regulators in various physiological processes and crucial contributors to the development of new anti-infective drugs. Although existing graph-based deep learning methods effectively model multi-label peptide representation, they often fail to incorporate multi-modal feature representation and extract multi-scale features from various views. To address these limitations, we present a multi-view framework for multi-label bioactive peptide classification based on multi-modal representation Learning by combining amino acid sequences and fusion molecular fingerprints. The peptide molecular graph is constructed by extracting the topological information and node features, respectively. Multi-view branches are designed by developing sequence-based and graph-based models to leverage their distinct and complementary strengths. Specifically, the protein language model ESM-2 is utilized to extract residue features from amino acid sequences deeply. Meanwhile, local features from molecular fingerprints are learned through a Filter Response Normalization layer and a Thresholded Linear Unit. At the same time, the Mamba module is innovatively employed to extract long-range dependencies and reduce time complexity. Our model demonstrates significantly enhanced and robust performance in multi-label bioactive peptide prediction tasks compared with state-of-the-art models, achieving 82.5% coverage, 80.9% precision and 80.3% accuracy on the MFBP dataset. Furthermore, visual analyses demonstrate that the model can effectively capture features from multiple views and highlight the interpretability of the model through the decision process.
Keywords:
Bioactive peptides
Multi-modal representation learning
Protein language model
Feature extraction and fusion
Mamba
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
View more organizations