ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908325105369088 |
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| author | Xiao, Yijia Sun, Edward Jin, Yiqiao Wang, Qifan Wang, Wei |
| author_facet | Xiao, Yijia Sun, Edward Jin, Yiqiao Wang, Qifan Wang, Wei |
| contents | Understanding biological processes, drug development, and biotechnological advancements requires a detailed analysis of protein structures and functions, a task that is inherently complex and time-consuming in traditional protein research. To streamline this process, we introduce ProteinGPT, a state-of-the-art multimodal large language model for proteins that enables users to upload protein sequences and/or structures for comprehensive analysis and responsive inquiries. ProteinGPT integrates protein sequence and structure encoders with linear projection layers to ensure precise representation adaptation and leverages a large language model (LLM) to generate accurate, contextually relevant responses. To train ProteinGPT, we constructed a large-scale dataset of 132,092 proteins, each annotated with 20-30 property tags and 5-10 QA pairs per protein, and optimized the instruction-tuning process using GPT-4o. Experiments demonstrate that ProteinGPT effectively generates informative responses to protein-related questions, achieving high performance on both semantic and lexical metrics and significantly outperforming baseline models and general-purpose LLMs in understanding and responding to protein-related queries. Our code and data are available at https://github.com/ProteinGPT/ProteinGPT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11363 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding Xiao, Yijia Sun, Edward Jin, Yiqiao Wang, Qifan Wang, Wei Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Biomolecules Understanding biological processes, drug development, and biotechnological advancements requires a detailed analysis of protein structures and functions, a task that is inherently complex and time-consuming in traditional protein research. To streamline this process, we introduce ProteinGPT, a state-of-the-art multimodal large language model for proteins that enables users to upload protein sequences and/or structures for comprehensive analysis and responsive inquiries. ProteinGPT integrates protein sequence and structure encoders with linear projection layers to ensure precise representation adaptation and leverages a large language model (LLM) to generate accurate, contextually relevant responses. To train ProteinGPT, we constructed a large-scale dataset of 132,092 proteins, each annotated with 20-30 property tags and 5-10 QA pairs per protein, and optimized the instruction-tuning process using GPT-4o. Experiments demonstrate that ProteinGPT effectively generates informative responses to protein-related questions, achieving high performance on both semantic and lexical metrics and significantly outperforming baseline models and general-purpose LLMs in understanding and responding to protein-related queries. Our code and data are available at https://github.com/ProteinGPT/ProteinGPT. |
| title | ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2408.11363 |