Holographic-(V)AE: An end-to-end SO(3)-equivariant (variational) autoencoder in Fourier space
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View abstract on PubMed
Summary
This summary is machine-generated.Holographic (variational) autoencoders leverage group-equivariance for unsupervised learning in 3D. This method extracts rotationally invariant embeddings and orientations for efficient data representation and downstream tasks like protein-ligand binding affinity prediction.
Area Of Science
- Artificial Intelligence
- Machine Learning
- Computational Science
Background
- Group-equivariant neural networks utilize generalized convolutions to model symmetric data.
- Advances have been made in supervised and unsupervised learning tasks, but symmetry-aware representations are underexplored.
Purpose Of The Study
- Introduce the holographic (variational) autoencoder (H-(V)AE) for end-to-end SO(3)-equivariant unsupervised learning in 3D.
- Develop a method to extract informative, low-dimensional representations from data with specified origin and symmetries.
Main Methods
- H-(V)AE is an SO(3)-equivariant autoencoder operating in Fourier space.
- It reconstructs spherical Fourier encodings, learning a latent space with rotationally invariant embeddings and equivariant orientation frames.
Main Results
- H-(V)AE efficiently encodes categorical features of spherical images.
- Learned latent spaces provide compact embeddings for protein structure microenvironments.
- State-of-the-art protein-ligand binding affinity predictions achieved when H-VAE embeddings are paired with random forest regressors.
Conclusions
- H-(V)AE offers a powerful framework for unsupervised learning and data generation with symmetries.
- The extracted low-dimensional representations are valuable for data-scarce downstream applications, particularly in computational biology.
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