AoyuJiao/DeepPose: Release v1.0.0: Initial Public Release - The Edge Computing Milestone

We are excited to announce the first official release of DeepPose, an integrated high-performance platform for protein-ligand docking. This version, v1.0.0-WSL2-Workstation, is specifically optimized for Windows users, providing a seamless "Edge Computing" experience with native Linux performance. 📖 Documentation & Manual The User Manual contains detailed hardware requirements, installation guides, and automated workflow tutorials. Download DeepPose User Manual (PDF) 🎓 How to Cite As the formal academic manuscript for DeepPose is currently in preparation, please cite this project as follows if it contributes to your research: Jiao, A. DeepPose: An integrated high-performance platform for protein-ligand docking (Version 1.0.0). GitHub: [Your Repository Link], 2026. 🚀 Key Features Integrated Multi-Scale Architecture: Unified environment supporting everything from local workstations to HPC clusters. Deep Learning Powered Engines: FABind: End-to-end geometric deep learning for rapid binding pose prediction. GNINA 1.3: Advanced CNN-based scoring with knowledge distillation. Protein Language Support: Built-in ESM-2 (650M) model for robust generalization. Research Safety Locks: Immutable core assets using chattr +i to ensure Bit-level reproducibility. 🛠 Installation & Distribution To deploy this release, you need the .tar workstation image. [!IMPORTANT] Physical Distribution Notice: Due to the massive size of the workstation image (60GB+), it is not suitable for direct network distribution. Users may apply for a physical USB drive containing the pre-packaged image by contacting the developer at bg2gcx@foxmail.com. Once you have the image file, import it into WSL2: wsl --import DeepPose_v1 📄 License This distribution is licensed under the GNU General Public License v2.0 (GPL v2.0). Full Changelog: https://github.com/AoyuJiao/DeepPose/commits/v1.0.0