Multimodal Protein

Multimodal protein research focuses on integrating diverse protein representations—amino acid sequences, 3D structures, and functional descriptions—to improve prediction and generation capabilities. Current efforts leverage deep learning architectures, including graph neural networks, transformers, and variational autoencoders, often within multimodal frameworks to learn comprehensive protein representations from combined data sources. This approach promises to significantly advance protein engineering, drug discovery, and our fundamental understanding of protein function by enabling more accurate predictions and the generation of novel protein designs based on multiple input modalities. The integration of large language models further enhances the ability to translate between these different representations, facilitating more holistic analyses.