首个统一框架的蛋白质结构生成基准，支持公平比较不同方法。
Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design
- 构建统一训练框架，整合多种基于SE(3)的生成模型。
- 在相同数据与评估下，全面对比6种先进蛋白骨架生成方法。
- 提供数学抽象层，加速新算法开发，适合结构生物学与生成模型研究者。
基于SE(3)的生成模型在蛋白质几何建模和结构设计中展现出巨大潜力，但当前缺乏模块化基准以实现方法的全面评估与公平比较。本文提出Protein-SE(3)，一个基于统一训练框架的新基准，包含蛋白骨架任务、集成生成模型、高层数学抽象及多样化评估指标。该框架整合了来自不同视角的先进生成模型：基于DDPM的Genie1与Genie2、基于评分匹配的FrameDiff与RfDiffusion，以及基于流匹配的FoldFlow与FrameFlow。所有方法均在相同训练数据集和评估指标下进行公平对比。此外，我们提供了生成模型背后的数学基础的高层抽象，使未来算法可快速原型化，无需依赖显式蛋白质结构。我们首次发布基于统一框架的完整基准，公开获取于https://github.com/BruthYU/protein-se3。
原文摘要 · Abstract (English)
SE(3)-based generative models have shown great promise in protein geometry modeling and effective structure design. However, the field currently lacks a modularized benchmark to enable comprehensive investigation and fair comparison of different methods. In this paper, we propose Protein-SE(3), a new benchmark based on a unified training framework, which comprises protein scaffolding tasks, integrated generative models, high-level mathematical abstraction, and diverse evaluation metrics. Recent advanced generative models designed for protein scaffolding, from multiple perspectives like DDPM (Genie1 and Genie2), Score Matching (FrameDiff and RfDiffusion) and Flow Matching (FoldFlow and FrameFlow) are integrated into our framework. All integrated methods are fairly investigated with the same training dataset and evaluation metrics. Furthermore, we provide a high-level abstraction of the mathematical foundations behind the generative models, enabling fast prototyping of future algorithms without reliance on explicit protein structures. Accordingly, we release the first comprehensive benchmark built upon unified training framework for SE(3)-based protein structure design, which is publicly accessible at https://github.com/BruthYU/protein-se3.
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