What is SimpleFold？
SimpleFold is a lightweight protein folding prediction AI model developed by Apple. The model is based on Flow Matching technology, skipping complex modules such as Multiple Sequence Alignment (MSA). It directly generates the 3D structure of proteins from random noise, significantly reducing computational costs. In authoritative benchmarks such as CAMEO22 and CASP14, SimpleFold demonstrated excellent performance. Without relying on expensive MSA and triangle attention mechanisms, it achieves results comparable to leading models (e.g., AlphaFold2, RoseTTAFold2). Even its smaller versions (e.g., SimpleFold-100M) remain highly efficient and competitive.
Key Features of SimpleFold
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Efficient 3D protein structure prediction: Quickly generates protein 3D structures from amino acid sequences.
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Reduced computational costs: Consumes far fewer resources compared to traditional models like AlphaFold2.
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Support for research and applications: Enables efficient studies in drug discovery, new material development, and beyond.
Technical Principles of SimpleFold
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Flow Matching Model: At the core of SimpleFold, Flow Matching learns smooth paths from random noise to target data, directly generating protein 3D structures. Built on continuous-time stochastic differential equations (SDEs), it reduces both computation steps and resource consumption, making it more efficient than traditional diffusion models.
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No reliance on complex modules: Unlike conventional protein folding models that depend on MSA, pairwise interaction graphs, or triangle updates, SimpleFold eliminates such components. This simplification lowers computational complexity, making the model more flexible and scalable.
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General-purpose neural network architecture: Instead of task-specific custom architectures, SimpleFold leverages general neural network modules. This makes it adaptable to different protein structure prediction tasks. Its performance can be further improved by scaling up model parameters and training data.
Project Resources
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GitHub Repository: https://github.com/apple/ml-simplefold
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arXiv Technical Paper: https://arxiv.org/pdf/2509.18480v1
Application Scenarios of SimpleFold
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Drug discovery: Accelerates drug design and screening by rapidly and accurately predicting protein structures, reducing R&D costs.
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Disease research: Helps scientists understand the role of proteins in diseases and provides insights for therapeutic development.
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New material development: Enables prediction of protein 3D structures to support innovations in biomaterials and nanotechnology.
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Fundamental scientific research: Simplifies protein folding studies, supporting deeper exploration of biomolecular structure and function.
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Biotechnology applications: Enhances efficiency and accuracy in enzyme engineering, vaccine design, and related fields.
