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.
@article{arxiv.2407.03380,
title = {Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties},
author = {Srivathsan Badrinarayanan and Chakradhar Guntuboina and Parisa Mollaei and Amir Barati Farimani},
journal= {arXiv preprint arXiv:2407.03380},
year = {2024}
}