  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, a
protein folding framework that supports significant components of
state-of-the-art models in the manner of an 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
give 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.
