GUI_UniDock provides a user-friendly graphical interface for Uni-Dock, a cutting-edge docking engine that leverages GPU acceleration to achieve up to 10-fold speedup over traditional CPU methods. This project simplifies high-throughput virtual screening, allowing researchers to manage complex docking simulations with a good precision.
- 10-fold Acceleration: Harnesses the full power of CUDA-enabled GPUs for massive-scale screening.
- Multimodal Scoring: Full support for
vina,vinardo, andad4scoring functions. - Interactive Grid Calibration: Real-time 3D visualization and adjustment of the docking search space.
- Job Telemetry: Live monitoring of docking progress and computational resource utilization.
Benchmarks compared to traditional CPU-based Autodock Vina.
| Metric | Uni-Dock (GPU) | Traditional Vina (CPU) |
|---|---|---|
| Throughput | High-Velocity Screening | Standard Throughput |
| Acceleration | ~10x | 1x (Baseline) |
| Efficiency | Energy-Optimized | High CPU Overhead |
- Python: 3.10 or higher.
- Uni-Dock Engine: Must be installed as per the official guide.
# Clone this repository
git clone [https://github.com/vedasoham/GUI_unidock.git](https://github.com/vedasoham/GUI_unidock.git)
cd GUI_unidock
# Initialize the environment
python setup.py
# Launch the interface
python app.pyThis project is licensed under the GPL-3.0 License - see the LICENSE file for details.
If you utilize the GUI interface of the tool then please cite the original framework and our graphical implementation:
- Original Framework: Yu, Yuejiang, et al. "Uni-dock: Gpu-accelerated docking enables ultralarge virtual screening." Journal of chemical theory and computation 19.11 (2023): 3336-3345
- Source Implementation: Original Uni-Dock GitHub.
- **Our Github Repository: [Uni-Dock GUI interface] (https://github.com/vedasoham/GUI_unidock.git)
CONTACT for COLLABORATION: thedrsoham[at]gmail[dot]com | 2401001030[at]gbu[dot]edu[dot]in
Computational and eXperimental Biomolecular Lab
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