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-the-art 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，都通过可靠的基准框架进行了比较和验证。继AlphaFold2之后，蛋白质折叠任务进入了一个新阶段，许多基于AlphaFold2组件的方法被提出。在蛋白质折叠领域，统一研究框架的重要性在于其包含实现和基准测试，以便一致且公平地比较各类方法。为实现这一目标，我们提出了Solvent，一个蛋白质折叠框架，它以即用型接口的方式支持最先进模型的重要组件。Solvent包含在统一代码库中实现的不同模型，并支持在相同数据集上对已定义模型进行训练和评估。我们对著名算法及其组件进行了基准测试，并提供了实验，这些实验为蛋白质结构建模领域提供了有价值的见解。我们希望Solvent能够提高所提出模型的可靠性和一致性，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。代码可在https://github.com/kakaobrain/solvent获取，该项目将持续开发。