  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.
