SimpleFold：蛋白质折叠比你想象的更简单

摘要

蛋白质折叠模型通常通过将领域知识融入架构模块和训练流程中取得了突破性成果。然而，鉴于生成模型在不同但相关问题上取得的成功，人们自然会质疑这些架构设计是否是构建高性能模型的必要条件。本文中，我们提出了SimpleFold，这是首个基于流匹配的蛋白质折叠模型，仅使用通用Transformer模块。传统的蛋白质折叠模型通常采用计算成本高昂的模块，包括三角更新、显式配对表示或为该特定领域定制的多重训练目标。相比之下，SimpleFold采用带有自适应层的标准Transformer模块，并通过生成流匹配目标及附加的结构项进行训练。我们将SimpleFold扩展至30亿参数，并在约900万蒸馏蛋白质结构及实验PDB数据上进行训练。在标准折叠基准测试中，SimpleFold-3B相较于最先进的基线模型展现出竞争力，此外，SimpleFold在集成预测中表现优异，这对于通过确定性重建目标训练的模型通常较为困难。得益于其通用架构，SimpleFold在消费级硬件上的部署和推理效率显著。SimpleFold挑战了蛋白质折叠中对复杂领域特定架构设计的依赖，为未来的进展开辟了新的设计空间。

English

Protein folding models have achieved groundbreaking results typically via a combination of integrating domain knowledge into the architectural blocks and training pipelines. Nonetheless, given the success of generative models across different but related problems, it is natural to question whether these architectural designs are a necessary condition to build performant models. In this paper, we introduce SimpleFold, the first flow-matching based protein folding model that solely uses general purpose transformer blocks. Protein folding models typically employ computationally expensive modules involving triangular updates, explicit pair representations or multiple training objectives curated for this specific domain. Instead, SimpleFold employs standard transformer blocks with adaptive layers and is trained via a generative flow-matching objective with an additional structural term. We scale SimpleFold to 3B parameters and train it on approximately 9M distilled protein structures together with experimental PDB data. On standard folding benchmarks, SimpleFold-3B achieves competitive performance compared to state-of-the-art baselines, in addition SimpleFold demonstrates strong performance in ensemble prediction which is typically difficult for models trained via deterministic reconstruction objectives. Due to its general-purpose architecture, SimpleFold shows efficiency in deployment and inference on consumer-level hardware. SimpleFold challenges the reliance on complex domain-specific architectures designs in protein folding, opening up an alternative design space for future progress.

SimpleFold：蛋白质折叠比你想象的更简单

SimpleFold: Folding Proteins is Simpler than You Think

摘要

Support