ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding
<|reference_start|>ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding: Understanding biological processes, drug development, and biotechnological advancements requires detailed analysis of protein structures and sequences, a task in protein research that is inherently complex and time-consuming when performed manually. To streamline this process, we introduce ProteinGPT, a state-of-the-art multi-modal protein chat system, that allows users to upload protein sequences and/or structures for comprehensive protein analysis and responsive inquiries. ProteinGPT seamlessly integrates protein sequence and structure encoders with linear projection layers for precise representation adaptation, coupled with a large language model (LLM) to generate accurate and contextually relevant responses. To train ProteinGPT, we construct a large-scale dataset of 132,092 proteins with annotations, and optimize the instruction-tuning process using GPT-4o. This innovative system ensures accurate alignment between the user-uploaded data and prompts, simplifying protein analysis. Experiments show that ProteinGPT can produce promising responses to proteins and their corresponding questions.<|reference_end|>
@article{xiao2024proteingpt:,
    title={ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure
  Understanding},
    author={Yijia Xiao, Edward Sun, Yiqiao Jin, Qifan Wang, Wei Wang},
    journal={arXiv preprint arXiv:2408.11363},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11363},
    primaryClass={cs.AI cs.CE cs.LG q-bio.BM}
}