AbODE：使用联合 ODE 的从头抗体设计
AbODE: Ab Initio Antibody Design using Conjoined ODEs
AI总结：
本文提出 AbODE 生成模型，通过扩展图 PDE 并采用单轮全量解码与连续微分注意力，联合设计抗体序列和三维结构，在基准指标上显著优于现有方法。
摘要：
抗体是能够中和病原体的 Y 形蛋白质，构成了我们适应性免疫系统的核心。从头生成靶向特定抗原的新抗体，是加速疫苗发现的关键。然而，氨基酸序列与三维结构的这种协同设计，包含并凸显了来自多个任务的核心挑战，包括蛋白质折叠（序列到结构）、逆向折叠（结构到序列）以及对接（结合）。我们力图通过一种新的生成模型 AbODE 克服这些挑战，该模型扩展了图 PDE，以同时容纳上下文信息和外部相互作用。与现有方法不同，AbODE 使用单轮全量解码，并引出连续的微分注意力；这种注意力封装了抗体内部以及涉及抗原的潜在相互作用，并随这些相互作用演化。我们揭示了 AbODE 与时间网络以及图匹配网络之间的基础联系。所提出模型在各基准的标准指标上显著优于现有方法。
英文摘要：
Antibodies are Y-shaped proteins that neutralize pathogens and constitute the core of our adaptive immune system. De novo generation of new antibodies that target specific antigens holds the key to accelerating vaccine discovery. However, this co-design of the amino acid sequence and the 3D structure subsumes and accentuates some central challenges from multiple tasks, including protein folding (sequence to structure), inverse folding (structure to sequence), and docking (binding). We strive to surmount these challenges with a new generative model AbODE that extends graph PDEs to accommodate both contextual information and external interactions. Unlike existing approaches, AbODE uses a single round of full-shot decoding and elicits continuous differential attention that encapsulates and evolves with latent interactions within the antibody as well as those involving the antigen. We unravel fundamental connections between AbODE and temporal networks as well as graph-matching networks. The proposed model significantly outperforms existing methods on standard metrics across benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
