Basic Usage

This guide covers the fundamental usage patterns of PandaDock.

Available Commands

PandaDock provides several command-line tools:

Core Commands

pandadock: Main docking interface
pandadock-flex: Flexible/induced-fit docking
pandadock-metal: Metal coordination docking
pandadock-ml: ML-enhanced docking
pandadock-tethered: Tethered/constrained docking

Utility Commands

pandadock-prepare: Prepare ligands (add H, generate 3D)
pandadock-gridbox: Generate grid box configurations
pandadock-report: Generate analysis reports

Listing Algorithms

To see all available algorithms:

pandadock list-algorithms

This will show:

CPU algorithms
GPU algorithms (if CUDA is available)
Scoring functions

Command-Line Options

Common options for the pandadock dock command:

Required:

-r, --receptor: Receptor PDB file
-l, --ligand: Ligand file (SDF/MOL2/PDB)
--center X Y Z or --grid-config: Grid box specification

Algorithm Selection:

-a, --algorithm: Docking algorithm (default: enhanced_hierarchical_cpu)
-s, --scoring: Scoring function (default: physics_based)

Output:

-o, --output-dir: Output directory (default: docking_output)
-n, --num-poses: Number of poses to generate (default: 20)

Performance:

--gpu: Enable GPU acceleration
--cpuworkers N: Number of CPU worker threads
--fast: Fast mode for quick testing

Advanced:

--ensemble: Use Boltzmann ensemble averaging
--rescoring: Rescoring method (none, mmgbsa)
--visualize: Generate visualization plots

Example Workflows

Virtual Screening

pandadock dock -r protein.pdb -l library.sdf \
               --algorithm monte_carlo_cpu \
               --fast --num-poses 5 \
               -o screening/

Lead Optimization

pandadock dock -r protein.pdb -l lead.sdf \
               --algorithm enhanced_hierarchical_cpu \
               --scoring hybrid \
               --rescoring mmgbsa \
               -o optimization/

Getting Help

For detailed help on any command:

pandadock --help
pandadock dock --help
pandadock-flex --help