Gaussian Process Temporal Embedding for Protein Simulations and Transitions
GP-TEMPEST is a PyTorch implementation of the Gaussian Process Variational Autoencoder (GP-VAE) framework for time-aware dimensionality reduction of molecular dynamics (MD) simulations. The method leverages physics-informed Gaussian Process priors to capture temporal correlations in the latent space, enabling the recovery of hidden or kinetically relevant degrees of freedom in complex biomolecular systems.
- Physics-informed dimensionality reduction using Gaussian Processes as temporal priors
- Flexible kernel selection, with default support for the Matérn kernel
- Sparse GP inference with inducing points for scalability to large molecular trajectories
- Compatible with large MD datasets and batch-wise training
If you use GP-TEMPEST in your research or teaching, please cite the following paper:
G. Diez, N. Dethloff, G. Stock,
"Recovering Hidden Degrees of Freedom Using Gaussian Processes,"
(2025). https://arxiv.org/abs/2505.18072