ESMFold2 将语言模型用于蛋白质设计，解决了抗体和蛋白结合剂设计这一基础难题，做药物发现和生物工程的团队可以直接用这个开源模型加速新药研发。
Alex Rives 团队宣布推出 ESMFold2，一个开源的科学引擎，用于蛋白质预测、设计和发现。该模型在蛋白质相互作用（尤其是抗体）方面达到最先进水平，这是治疗药物的关键模态。团队针对五个重要的癌症和免疫学治疗靶点，设计并验证了迷你蛋白结合剂和单链抗体，成功率极高，亲和力达到治疗活性水平。同时发布了包含 68 亿蛋白质和 11 亿预测结构的图谱。ESMFold2 基于在数十亿蛋白质序列上训练的语言模型，通过语言建模自然涌现出蛋白质生物学的世界模型，其表征空间反映了通过一个世纪实证科学积累的理解。
Congrats on the launch! https://t.co/TnFtzkwMqe
Congrats on the launch! x.com/alexrives/stat… Alex Rives @alexrives Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology. The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics. We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity. We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures. ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences. A world model of protein biology emerges through language modeling. We’ve used the techniques of mechanistic interpretability developed to understand large language models to understand the concepts ESM uses to represent proteins. The model’s representation space has a compositional organization of features across scales, levels of complexity, and abstraction, that reflects and mirrors the understanding of protein biology developed through a century of empirical science. This understanding emerges without prior knowledge, just from language modeling of protein sequences. Language models are becoming a powerful substrate to understand and program biology. The design of protein interactions is one of the most fundamental problems in biophysics, and has critical implications for the discovery of new medicines. A simple gradient based search with the model was able to discover high-affinity protein binders. I'm excited by the potential this has to accelerate basic science and the understanding of proteins. And especially for the new avenues it opens up for therapeutic design and medicine. 🔗 View Quoted Tweet 💬 1 🔄 0 ❤️ 2 👀 691 📊 1 ⚡