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🧬 synth-saxs: Differentiable SAXS Simulation\n\n[![PyPI version](https://img.shields.io/pypi/v/synth-saxs.svg)](https://pypi.org/project/synth-saxs/)\n[![Supported Python versions](https://img.shields.io/pypi/pyversions/synth-saxs.svg)](https://pypi.org/project/synth-saxs/)\n[![Tests](https://github.com/elkins/synth-saxs/actions/workflows/test.yml/badge.svg)](https://github.com/elkins/synth-saxs/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\n`synth-saxs` is a JAX-powered simulator for back-calculating Small-Angle X-ray Scattering (SAXS) profiles from protein coordinates.\n\n---\n\n### 🧪 For Structural Biologists\n*   **Guinier Analysis:** Automatically calculate Radius of Gyration ($R_g$) and forward scattering $I(0)$.\n*   **Hydration Modeling:** Explicit support for solvation shell effects on scattering intensity.\n\n### 🤖 For Machine Learning Geeks\n*   **Differentiable Debye:** The entire Debye formula is implemented in JAX, allowing gradients to flow from the scattering profile back to atomic positions.\n*   **Fast Integration:** JIT-compilable kernels for sub-millisecond SAXS prediction from large structures.\n\n---\n\n## 📦 Installation\n\n```bash\npip install synth-saxs\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%2Fsynth-saxs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Felkins%2Fsynth-saxs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felkins%2Fsynth-saxs/lists"}