arXiv：cs.AI· Zhenqiao Song, Jianwen Xie, Lei Li·· 7 小时前
PIDiff：面向蛋白质界面设计的离散与 SE(3) 联合扩散框架
Joint Discrete and SE(3) Diffusion for Protein Interface Design
AI 导读
研究者提出 PIDiff，一个物理化学增强的多模态扩散框架，通过耦合离散序列空间扩散与连续 SE(3) 结构流形扩散，联合设计靶点条件蛋白质序列与结构。其核心 PIFormer 架构交替使用几何感知的不变点注意力与全局序列自注意力，并构建了基于 PDB 的蛋白质复合物数据集 PIBench。
正文
Abstract:The design of high-affinity, target-specific protein binders is central to the development of modern therapeutics. However, existing protein-generation methods remain limited in this setting, as many generated designs fail to simultaneously achieve geometric and physicochemical complementarity at binding interfaces. To address this challenge, we introduce PIDiff, a physicochemically enhanced multimodal diffusion framework for protein-protein complex design. PIDiff jointly designs target-conditioned protein sequences and structures by coupling diffusion in discrete sequence space with diffusion on the continuous (SE(3)) structural manifold. At its core, PIDiff employs PIFormer, a novel architecture that interleaves geometry-aware invariant point attention with global sequence self-attention. We further curate PIBench, a protein-protein complex dataset with diverse target scaffolds derived from the Protein Data Bank (PDB). We first pretrain PIDiff on PIBench and subsequently fine-tune it for two therapeutically relevant applications: minibinder design and antibody design. PPFrame consistently outperforms baseline methods across sequence design success rate, structure binding energy, and sequence-structure co-designability on all tasks.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14699 [cs.AI] |
| (or arXiv:2610.14699v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14699 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhenqiao Song [view email]
[v1]
Sat, 10 Oct 2026 08:16:12 UTC (2,993 KB)
来源：arXiv：cs.AI · arxiv.org