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：摘要：一致性与可靠性是开展人工智能研究的关键。许多知名研究领域，如目标检测，已通过可靠的基准框架进行了比较与验证。继 AlphaFold2 之后，蛋白质折叠任务进入新阶段，众多基于 AlphaFold2 组件的方法相继被提出。在蛋白质折叠研究中，一个统一的研究框架对于实现各种方法的一致、公平比较至关重要，该框架包含实现方案与基准测试。为此，我们提出 Solvent，一个支持以即用接口方式整合当前先进模型核心组件的蛋白质折叠框架。Solvent 在统一代码库中实现了多种模型，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了基准测试，并通过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 能够提升所提出模型的一致性与可靠性，同时在速度与成本方面实现高效，从而加速蛋白质折叠建模研究。代码已开源在 https://github.com/kakaobrain/solvent，该项目将持续发展。