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

• URL: http://arxiv.org/abs/2407.03380v1
• Date: Tue, 2 Jul 2024 20:13:47 GMT
• Title: Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties
• Authors: Srivathsan Badrinarayanan, Chakradhar Guntuboina, Parisa Mollaei, Amir Barati Farimani,
• Abstract summary: Multi-Peptide is an innovative approach that combines transformer-based language models with Graph Neural Networks (GNNs) to predict peptide properties. 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.
• Score: 5.812284760539713
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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