SimpleFold - Apple's open-source lightweight AI model for predicting protein folding.

What is SimpleFold?

SimpleFold is a lightweight protein folding prediction AI model from Apple. Based on flow matching technology, it skips complex modules such as multiple sequence alignment (MSA) and directly generates the 3D structure of proteins from random noise, significantly reducing computational costs. In authoritative benchmark tests such as CAMEO22 and CASP14, SimpleFold performs exceptionally well, achieving performance comparable to top models (such as AlphaFold2 and RoseTTAFold2) without requiring expensive MSA and triangular attention mechanisms. Smaller versions (such as SimpleFold-100M) are also highly efficient and competitive.

Main functions of SimpleFold

• Highly efficient prediction of protein three-dimensional structureRapidly generate the three-dimensional structure of a protein from its amino acid sequence.
• Reduce computing costsCompared to traditional models (such as AlphaFold2), it significantly reduces the consumption of computing resources.
• Support scientific research and applicationIt facilitates efficient research in fields such as drug development and new material exploration.

SimpleFold's technical principles

• Flow Matching ModelThe flow matching model is the core of SimpleFold. It directly generates the 3D structure of proteins by learning a smooth path from random noise to target data. Based on continuous-time stochastic differential equations (SDEs), it can significantly reduce computational steps and resource consumption, making it more efficient than traditional diffusion models.
• Do not depend on complex modulesSimpleFold does not rely on complex modules commonly used in traditional protein folding models, such as multiple sequence alignment (MSA), pairwise interaction graphs, and triangular updates. This simplified design reduces computational complexity, making the model more flexible and easily scalable.
• General architecture moduleSimpleFold is based on a general neural network architecture, rather than a complex architecture tailored for protein folding tasks. This makes the model more flexible and adaptable to different protein structure prediction tasks. Furthermore, the performance of SimpleFold can be further improved by increasing the model's parameter size and the amount of training data.

SimpleFold project address

• GitHub repositoryhttps://github.com/apple/ml-simplefold
• arXiv technical paper: https://arxiv.org/pdf/2509.18480v1

Application scenarios of SimpleFold

• Drug developmentBy rapidly and accurately predicting protein structures, it can accelerate drug design and screening processes and reduce R&D costs.
• Disease researchIt helps scientists understand the role of proteins in diseases and provides a basis for developing treatments.
• New materials developmentPredicting the three-dimensional structure of proteins supports innovation in biomaterials and nanotechnology.
• Basic scientific researchThis simplifies the research process of protein folding and helps the academic community to explore the structure and function of biomolecules in greater depth.
• Biotechnology applicationsSimpleFold can be used to improve efficiency and accuracy in fields such as enzyme engineering and vaccine design.