正文：
近日，腾讯AI实验室、北京大学深圳研究生院和西京消化病医院的研究团队在抗体设计领域取得重大进展，他们的研究成果发表在《Nature Communications》上。该团队提出了一种预训练抗体生成大语言模型（PALM-H3），用于从零开始生成具有特定抗原结合特异性的抗体CDRH3。这项技术的突破有望减少对天然抗体的依赖，加速抗体药物的开发进程。
传统的抗体设计依赖于从动物血清中分离出的天然抗体，这是一个耗时且资源密集的过程。而PALM-H3模型能够利用人工智能技术，直接生成具有特定抗原结合特性的抗体CDRH3，极大地简化了抗体设计流程。此外，研究团队还设计了一个高精度的抗原-抗体结合预测模型A2binder，能够准确预测抗体的结合特异性和亲和力，确保生成的抗体具有实际应用价值。
PALM-H3模型的工作原理是通过模仿自然语言处理领域的大规模预训练模型，学习蛋白质序列的表征模式。它使用基于ESM2的抗原模型作为编码器，抗体Roformer作为解码器，生成CDRH3序列。而A2binder模型则通过对接实验和AI方法相结合，评估抗体对特定抗原的亲和力。
这项研究不仅为抗体设计提供了新的思路，还为生物治疗药物的研发开辟了新的途径。随着技术的不断进步，未来将有更多的抗体药物能够被高效、快速地开发出来，为人类健康带来更多的希望。
英语如下：
News Title: “Innovative Breakthrough: Tencent-Peking University Team Publishes New Antibody Generation Model in Nature Journal”
Keywords: Antibody Generation, Pre-trained Model, Nature Journal
News Content:
Title: Tencent-Peking University Team Develops New Method for Antibody Design Breakthrough
Article:
Recently, a research team from Tencent AI Laboratory, the Shenzhen Graduate School of Peking University, and Xijing Digestive Disease Hospital made a significant breakthrough in the field of antibody design, with their findings published in Nature Communications. The team proposed a pre-trained antibody generation large language model (PALM-H3) for generating CDRH3 regions of antibodies with specific antigen binding specificity from scratch. This technological breakthrough is expected to reduce dependence on natural antibodies and accelerate the development of antibody drugs.
Traditional antibody design relies on natural antibodies isolated from animal sera, a time-consuming and resource-intensive process. The PALM-H3 model utilizes artificial intelligence technology to directly generate CDRH3 regions of antibodies with specific antigen binding characteristics, greatly simplifying the antibody design process. Additionally, the research team designed a high-precision antigen-antibody binding prediction model, A2binder, which can accurately predict the binding specificity and affinity of antibodies, ensuring that the generated antibodies have practical application value.
The working principle of the PALM-H3 model is to mimic the large-scale pre-training models in the natural language processing field, learning the representation patterns of protein sequences. It employs an antigen model based on ESM2 as an encoder and an antibody Roformer as a decoder to generate CDRH3 sequences. Meanwhile, the A2binder model combines docking experiments and AI methods to evaluate the affinity of antibodies for specific antigens.
This research not only provides a new approach for antibody design but also opens up new avenues for the development of biotherapeutic drugs. As technology continues to advance, more antibody drugs will be efficiently and rapidly developed in the future, bringing more hope for human health.
【来源】https://www.jiqizhixin.com/articles/2024-08-16-7
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