Consistency and reliability are crucial for conducting AI research. Many famous research fields, such as object detection, have been compared and validated with solid benchmark frameworks. After AlphaFold2, the protein folding task has entered a new phase, and many methods are proposed based on the component of AlphaFold2. The importance of a unified research framework in protein folding contains implementations and benchmarks to consistently and fairly compare various approaches. To achieve this, we present Solvent, an protein folding framework that supports significant components of state-of-th-arts models in the manner of off-the-shelf interface Solvent contains different models implemented in a unified codebase and supports training and evaluation for defined models on the same dataset. We benchmark well-known algorithms and their components and provide experiments that give helpful insights into the protein structure modeling field. We hope that Solvent will increase the reliability and consistency of proposed models and gives efficiency in both speed and costs, resulting in acceleration on protein folding modeling research. The code is available at https://github.com/kakaobrain/solvent, and the project will continue to be developed.

翻译：一致性和可靠性对于开展人工智能研究至关重要。许多著名研究领域，如目标检测，已通过坚实的基准框架进行比较和验证。继AlphaFold2之后，蛋白质折叠任务进入新阶段，众多方法基于AlphaFold2的组件提出。蛋白质折叠研究中统一框架的重要性在于，它需包含实现与基准测试，从而一致且公平地比较各种方法。为此，我们提出Solvent——一种蛋白质折叠框架，以即插即用的接口支持当前最先进模型的关键组件。Solvent在统一代码库中实现多种模型，并支持在同一数据集上对已定义模型进行训练与评估。我们对知名算法及其组件进行基准测试，并提供实验，这些实验为蛋白质结构建模领域提供了有益见解。我们希望Solvent能提升所提出模型的一致性和可靠性，并在速度和成本方面提高效率，从而加速蛋白质折叠建模研究。代码已发布于https://github.com/kakaobrain/solvent，项目将持续开发。