GNINA
Enhanced molecular docking with deep learning.
GNINA is an open-source molecular docking tool that integrates convolutional neural networks (CNNs) to enhance the accuracy of protein-ligand binding predictions. By leveraging deep learning, GNINA improves pose prediction and scoring, facilitating more reliable virtual screening and drug discovery processes.
Estimated resource cost: 0.0002945
Categories: Ligand/Drug Design & Screening, Molecular Docking & Interactions
Tags: Affinity Prediction Protein–Ligand Docking Virtual Screening
Key Capabilities
- Integrates CNNs for improved scoring and optimization of ligand binding poses.
- Outperforms traditional docking tools like AutoDock Vina in pose prediction accuracy.
- Supports flexible receptor docking to account for protein flexibility during ligand binding.
- Offers customizable scoring functions, including empirical and machine learning-based options.
Runtime Statistics
| Metric | Value |
|---|---|
| runtime_mean | 134 |
| runtime_median | 25 |
| runtime_std | 1346 |
| runtime_90th_percentile | 102 |
| runtime_max | 55409 |
Similar Tools
- AutoDock Vina (smina)
- DiffDock-L
- DynamicBind
- SPRINT
Ready to submit your job?
Review your configuration, then confirm the estimated credit cost before you run the job. Note that credit estimates are not guaranteed and runtime can vary depending on inputs and settings.
Estimated Credits: 0.0002945
Invite-only, limited-time access. Please contact ztang@getantibody.com.
Invite-only, limited-time access. Please contact ztang@getantibody.com.