AI 中文总结
研究针对蛋白质设计中可变长度生成的关键问题，引入广义泊松流（GPFlow）框架，通过最小化负对数似然学习速率函数，经多模态评估验证其可变长度生成质量，在多种设计任务中表现出色，提升了蛋白质设计的灵活性与适应性。
AI 中文摘要
在蛋白质设计中，生成可变长度蛋白质的能力至关重要，因为最佳长度通常未知且与可设计性紧密相关。当前基于扩散和流的生成模型通常需要在采样前指定蛋白质长度，限制了其探索可行设计空间的灵活性。为解决此限制，我们引入广义泊松流（GPFlow），这是一个可变长度生成框架，通过最小化其负对数似然来学习非齐次广义泊松过程的速率函数。我们建立了恢复联合多模态分布的总体水平保证，并推导了数据分布与生成分布之间KL散度的上界。我们在结构和序列设计、基序支架和肽协同设计等方面全面评估了GPFlow，跨越欧几里得、分类和黎曼模态，以充分验证其可变长度生成质量。在无条件设计中，与相应的固定长度基线相比，GPFlow提高了结构可设计性，并在序列设计中实现了最佳的分布适应性，同时完美恢复了长度分布。在条件基序支架中，GPFlow在16个基于结构的设计任务中的10个上排名第一，具有更多独特的成功案例，并且在基于序列的设计中也完成了更多通过的任务。在肽协同设计中，即使无法访问原生长度预言机，GPFlow也保持竞争力。
英文摘要
The ability to generate variable-length proteins is crucial in protein design, where the optimal length is often unknown and tightly coupled to designability. Current diffusion- and flow-based generative models typically require the protein length to be specified before sampling, limiting their flexibility in exploring the feasible design space. To address this limitation, we introduce Generalized Poisson Flow (GPFlow), a variable-length generative framework that learns the rate function of an inhomogeneous generalized Poisson process by minimizing its negative log-likelihood. We establish population-level guarantees for recovering the joint multimodal distribution and derive an upper bound on the KL divergence between the data and generated distributions. We comprehensively evaluate GPFlow across structure and sequence design, motif scaffolding, and peptide co-design, spanning Euclidean, categorical, and Riemannian modalities to fully validate its variable-length generation quality. In unconditional design, GPFlow improves structural designability and achieves the best distributional fitness for sequence design compared to their corresponding fixed-length baselines, while perfectly recovering the length distribution. In conditional motif scaffolding, GPFlow ranks first on 10 of 16 structure-based design tasks with significantly more unique successes and also achieves more passed tasks in sequence-based design. In peptide co-design, GPFlow remains competitive even without access to a native-length oracle.
