Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910512542908416 |
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| author | Badrinarayanan, Srivathsan Guntuboina, Chakradhar Mollaei, Parisa Farimani, Amir Barati |
| author_facet | Badrinarayanan, Srivathsan Guntuboina, Chakradhar Mollaei, Parisa Farimani, Amir Barati |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03380 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties Badrinarayanan, Srivathsan Guntuboina, Chakradhar Mollaei, Parisa Farimani, Amir Barati Quantitative Methods Artificial Intelligence Machine Learning 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. |
| title | Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties |
| topic | Quantitative Methods Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.03380 |