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🧬 DiffEnsemble: Differentiable IDP Ensemble Prediction\n\n[![PyPI version](https://img.shields.io/pypi/v/diff-ensemble.svg)](https://pypi.org/project/diff-ensemble/)\n[![Supported Python versions](https://img.shields.io/pypi/pyversions/diff-ensemble.svg)](https://pypi.org/project/diff-ensemble/)\n[![Tests](https://github.com/elkins/diff-ensemble/actions/workflows/test.yml/badge.svg)](https://github.com/elkins/diff-ensemble/actions/workflows/test.yml)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![JAX](https://img.shields.io/badge/Accelerated_by-JAX-blue.svg)](https://github.com/google/jax)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n\nDiffEnsemble is a JAX-powered framework for predicting structural ensembles of Intrinsically Disordered Proteins (IDPs) using a Variational Autoencoder (VAE) coupled with differentiable biophysical observables.\n\n---\n\n### 🧪 For Structural Biologists\n*   **Ensemble Averaging:** Automatically calculates ensemble-averaged SAXS profiles and NMR observables.\n*   **Disorder Recovery:** Specifically designed for proteins that don't have a single \"fixed\" structure, providing a statistical view of the conformational landscape.\n\n### 🤖 For Machine Learning Geeks\n*   **VAE-Physics Integration:** A latent-space generative model where the reconstruction loss is a combination of latent KLD and physical observables (SAXS/NMR).\n*   **Differentiable Torsions:** Maps latent vectors to 3D coordinates via a differentiable NeRF (Natural Extension Reference Frame) implementation.\n\n---\n\n## 🚀 Key Features\n\n*   **JAX-Accelerated VAE:** High-performance training of generative models for IDPs.\n*   **Debye-Based SAXS Prediction:** Differentiable back-calculation of SAXS profiles from structural ensembles.\n*   **Latent Space Exploration:** Sample new conformations from the learned disordered landscape.\n\n## 📦 Installation\n\n```bash\npip install diff-ensemble\n```\n\n## 📖 Tutorials\n\nGet started immediately with our interactive Jupyter notebooks:\n\n*   **[Quick Start: Differentiable IDP Ensemble Prediction](examples/quickstart_ensemble.ipynb)**: Train a VAE to predict structural ensembles constrained by SAXS data.\n    [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elkins/diff-ensemble/blob/main/examples/quickstart_ensemble.ipynb)\n\n## 📜 License\n\nDistributed under the MIT License. See `LICENSE` for more information.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felkins%2Fdiff-ensemble","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Felkins%2Fdiff-ensemble","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felkins%2Fdiff-ensemble/lists"}