ProtChatGPT：利用大语言模型理解蛋白质
ProtChatGPT: Towards Understanding Proteins with Large Language Models
AI总结：
ProtChatGPT利用大语言模型通过自然语言交互理解蛋白质结构，结合蛋白质编码器、PLP-former和投影适配器，实现用户上传蛋白质并提问，实验证明其能产生有前景的响应。
摘要：
蛋白质研究在众多基础学科中至关重要，但理解其复杂的结构-功能关系仍然具有挑战性。近期，大语言模型（LLMs）在理解特定任务知识方面取得了显著进展，这表明专门针对蛋白质的类ChatGPT系统可能有助于促进基础研究。在这项工作中，我们介绍了ProtChatGPT，其旨在通过自然语言学习和理解蛋白质结构。ProtChatGPT使用户能够上传蛋白质、提出问题并进行交互式对话，以产生全面的答案。该系统由蛋白质编码器、蛋白质-语言相关变换器（PLP-former）、投影适配器和LLM组成。蛋白质首先经过蛋白质编码器和PLP-former产生蛋白质嵌入，然后由适配器进行投影以与LLM对齐。最后，LLM将用户问题与投影嵌入相结合，生成信息丰富的答案。实验表明，ProtChatGPT能够对蛋白质及其相应问题产生有前景的响应。我们希望ProtChatGPT能够为蛋白质研究的进一步探索和应用奠定基础。代码和我们的预训练模型将公开提供。
英文摘要：
Protein research is crucial in various fundamental disciplines, but understanding their intricate structure-function relationships remains challenging. Recent Large Language Models (LLMs) have made significant strides in comprehending task-specific knowledge, suggesting the potential for ChatGPT-like systems specialized in protein to facilitate basic research. In this work, we introduce ProtChatGPT, which aims at learning and understanding protein structures via natural languages. ProtChatGPT enables users to upload proteins, ask questions, and engage in interactive conversations to produce comprehensive answers. The system comprises protein encoders, a Protein-Language Pertaining Transformer (PLP-former), a projection adapter, and an LLM. The protein first undergoes protein encoders and PLP-former to produce protein embeddings, which are then projected by the adapter to conform with the LLM. The LLM finally combines user questions with projected embeddings to generate informative answers. Experiments show that ProtChatGPT can produce promising responses to proteins and their corresponding questions. We hope that ProtChatGPT could form the basis for further exploration and application in protein research. Code and our pre-trained model will be publicly available.
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