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🧬 ResonanceFlow: Differentiable Protein Structure Prediction\n\n[![PyPI version](https://img.shields.io/pypi/v/resonance-flow.svg)](https://pypi.org/project/resonance-flow/)\n[![Supported Python versions](https://img.shields.io/pypi/pyversions/resonance-flow.svg)](https://pypi.org/project/resonance-flow/)\n[![Tests](https://github.com/elkins/resonance-flow/actions/workflows/test.yml/badge.svg)](https://github.com/elkins/resonance-flow/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\nResonanceFlow is an end-to-end differentiable framework for protein structure prediction that incorporates NMR experimental constraints directly into the folding process.\n\n---\n\n### 🧪 For Structural Biologists\n*   **Experimental Self-Correction:** Instead of just predicting a structure, ResonanceFlow uses NMR observables (RDCs, NOEs, Chemical Shifts) to \"steer\" the model toward the experimental reality.\n*   **Physics-Grounded:** Built on the same biophysical kernels used in standard NMR suites, but optimized for the AI era.\n\n### 🤖 For Machine Learning Geeks\n*   **Differentiable Physics Loss:** The entire NMR back-calculation is implemented as a differentiable JAX operator, allowing gradients to flow from experimental residuals back to Transformer weights.\n*   **Structural Refinement:** Uses a coordination-space predictor that can be fine-tuned on a single protein using only its NMR spectrum as supervision.\n\n---\n\n## 🚀 Key Features\n\n*   **Differentiable NMR Kernels:** Back-calculate RDCs, NOEs, and Chemical Shifts with full gradient support.\n*   **Transformer-Based Folding:** Predicts 3D coordinates directly from amino acid sequences.\n*   **Self-Correction Loop:** Minimizes the residual between back-calculated and experimental spectra during inference.\n\n## 📦 Installation\n\n```bash\npip install resonance-flow\n```\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%2Fresonance-flow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Felkins%2Fresonance-flow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felkins%2Fresonance-flow/lists"}