标题： AbODE：使用连接常微分方程的从头抗体设计 显示英文标题

标题： AbODE: Ab Initio Antibody Design using Conjoined ODEs

摘要： 抗体是Y形的蛋白质，能够中和病原体，并构成我们适应性免疫系统的核心。 从头生成针对特定抗原的新抗体是加速疫苗发现的关键。 然而，这种氨基酸序列和三维结构的共同设计涵盖了并突出了来自多个任务的一些核心挑战，包括蛋白质折叠（序列到结构）、逆向折叠（结构到序列）和对接（结合）。 我们努力通过一种新的生成模型AbODE来克服这些挑战，该模型将图PDE扩展以适应上下文信息和外部相互作用。 与现有方法不同， AbODE使用一次完整的解码过程，并产生连续的微分注意力，该注意力封装并随着抗体内部以及与抗原相关的潜在相互作用而演变。 我们揭示了AbODE与时间网络以及图匹配网络之间的基本联系。 所提出的模型在标准指标上显著优于现有方法。 显示英文摘要

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.