自"阿尔法折叠2"颠覆蛋白质结构预测以来，人工智能（AI）技术在治疗性抗体设计领域展现出前所未 有的应用潜力。 《自然》网站在近日的报道中指出，近年来，多个科研团队利用自主开发的AI工具，辅助设计并成功 制备出多种功能各异的治疗性抗体。不过，专家同时提醒，这些抗体的安全性与有效性仍需进一步验 证。 抗体药物市场巨大 抗体是免疫系统中识别特定靶标并激发保护性反应的关键蛋白质。与所有蛋白质相同，抗体由氨基酸链 折叠形成复杂的三维空间结构，既能阻断病原体侵入细胞，也可标记异常细胞引导免疫系统清除，还能 用于递送药物或抑制疾病相关蛋白质的活性。目前全球已有160余种工程化抗体获批用于癌症、感染性 疾病及自身免疫性疾病的治疗。 英国牛津大学旗下《抗体治疗》杂志2022年的分析数据显示，随着数千种新型抗体不断涌现，预计到 2028年，全球抗体药物市场年收入将突破4550亿美元。正如诺贝尔奖得主、美国华盛顿大学蛋白质设计 专家戴维·贝克所言："抗体已成为制药领域的硬通货。" 然而，传统抗体开发流程通常面临周期长、成本高、挑战大的困境。候选抗体需经历多轮优化，才能具 备理想药物的特性，包括良好的溶解性、高度的特异性以及避免脱 ...

Core Insights - The article discusses the introduction of BoltzGen, a new model developed by MIT and several institutions to address the limitations of traditional protein design methods, which rely heavily on physical calculations and have high computational costs [1][2][3] Group 1: Model Overview - BoltzGen utilizes a unified all-atom generative model that replaces traditional discrete residue labels with geometric continuous representations, allowing for joint training of protein folding and complex design [2][3] - The model incorporates a flexible design specification language that enables controllable generation across different molecular types, enhancing design efficiency and interpretability [1][3] Group 2: Research Highlights - The model has demonstrated a 66% success rate in achieving nanomolar affinity for designed nanobodies and protein complexes, showcasing its ability to optimize folding and binding performance simultaneously [2][12] - BoltzGen's architecture integrates a trunk network for token representation and a diffusion module for generating three-dimensional structures, allowing for effective modeling of atomic relationships [10][11] Group 3: Experimental Validation - In experiments involving 26 targets, BoltzGen maintained a high success rate, achieving nanomolar affinity in 66% of cases for previously unseen complex targets [12][25] - The model has shown versatility in designing peptides that bind to various structures, including those related to acute myeloid leukemia and specific enzymes, with binding affinities ranging from nanomolar to micromolar levels [15][17][19] Group 4: Data Utilization - The training of BoltzGen involved a multi-modal dataset sourced from high-quality experimental structures, AlphaFold predictions, and generated complex structures, enhancing the model's generalization capabilities [7][9] - The research team ensured diversity in the training data by excluding over-sampled datasets, maintaining a broad generation space [9]