Summary of Proteingpt: Multimodal Llm For Protein Property Prediction and Structure Understanding, by Yijia Xiao et al.
ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding
by Yijia Xiao, Edward Sun, Yiqiao Jin, Qifan Wang, Wei Wang
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A multi-modal protein chat system called ProteinGPT is introduced for comprehensive protein analysis and responsive inquiries. The system integrates protein sequence and structure encoders with linear projection layers for precise representation adaptation, coupled with a large language model (LLM) to generate accurate responses. A large-scale dataset of 132,092 proteins with annotations was constructed to train ProteinGPT, which was optimized using GPT-4o. This innovative system ensures accurate alignment between user-uploaded data and prompts, simplifying protein analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ProteinGPT is a new way to understand proteins, drug development, and biotechnology advancements. It’s like having a super-smart computer that can answer questions about proteins for you! Right now, scientists have to do this work manually, which takes a long time. ProteinGPT makes it faster and more accurate by using special codes and a big language model. This means scientists can focus on making new discoveries instead of spending hours doing manual analysis. |
Keywords
» Artificial intelligence » Alignment » Gpt » Language model » Large language model » Multi modal
