CPU Algorithms
PandaDock provides 5 CPU-based docking algorithms optimized for different use cases.
Enhanced Hierarchical CPU
Algorithm ID: enhanced_hierarchical_cpu
Recommended default algorithm for high-accuracy general docking.
Description: 3-stage hierarchical search with progressive refinement from coarse to fine sampling.
Stages:
Global Search: Coarse exploration (2-5 Å steps, 30-60° rotations)
Local Refinement: Medium resolution (0.5-1 Å steps, 10-20° rotations)
Fine Optimization: High precision (0.1-0.3 Å steps, 2-5° rotations) with simulated annealing
Performance:
Mean RMSD: ~0.08 Å
Runtime: 150-250 seconds
Success rate: 95-98% (RMSD < 2Å)
Usage:
pandadock dock -r protein.pdb -l ligand.sdf \
--algorithm enhanced_hierarchical_cpu \
--center 10 20 30 --box 20 20 20
Monte Carlo CPU
Algorithm ID: monte_carlo_cpu
Best for: Fast screening and initial exploration
Description: Monte Carlo sampling with Metropolis criterion and simulated annealing for efficient conformational space exploration.
Algorithm:
Generate random initial pose
Apply random perturbation
Evaluate energy
Accept/reject based on Metropolis criterion
Gradually decrease temperature
Performance:
Mean RMSD: ~0.5-1.5 Å
Runtime: 30-60 seconds (fastest)
Success rate: 85-90% (RMSD < 2Å)
Usage:
pandadock dock -r protein.pdb -l ligand.sdf \
--algorithm monte_carlo_cpu \
--center 10 20 30 --box 20 20 20
Genetic Algorithm CPU
Algorithm ID: genetic_algorithm_cpu
Best for: Complex binding sites and flexible ligands
Description: Evolutionary algorithm that evolves a population of ligand poses through selection, crossover, and mutation.
Steps:
Initialize random population
Evaluate fitness
Tournament selection
Crossover (blend poses)
Mutation (random perturbations)
Elitism (preserve top 10%)
Performance:
Mean RMSD: ~0.3-0.8 Å
Runtime: 120-200 seconds
Success rate: 90-95% (RMSD < 2Å)
Usage:
pandadock dock -r protein.pdb -l ligand.sdf \
--algorithm genetic_algorithm_cpu \
--center 10 20 30 --box 20 20 20
Hierarchical CPU
Algorithm ID: hierarchical_cpu
Best for: Balanced accuracy and speed
Description: Standard 2-stage hierarchical search with grid-based exploration and local optimization.
Performance:
Mean RMSD: ~0.5-1.0 Å
Runtime: 60-100 seconds
Success rate: 88-92% (RMSD < 2Å)
Usage:
pandadock dock -r protein.pdb -l ligand.sdf \
--algorithm hierarchical_cpu \
--center 10 20 30 --box 20 20 20
Crystal Guided CPU
Algorithm ID: crystal_guided_cpu
Best for: Validation studies and reproducing crystal structures
Description: Uses crystallographic information to guide docking towards known binding modes.
Performance:
Mean RMSD: ~0.05-0.2 Å (with reference)
Runtime: 100-150 seconds
Success rate: 98-100% (with crystal structure)
Usage:
pandadock dock -r protein.pdb -l ligand.sdf \
--algorithm crystal_guided_cpu \
--reference-ligand crystal_ligand.pdb \
--center 10 20 30 --box 20 20 20
Algorithm Selection Guide
By Use Case
General docking:
enhanced_hierarchical_cpuFast screening:
monte_carlo_cpuComplex sites:
genetic_algorithm_cpuValidation:
crystal_guided_cpu
By Ligand Flexibility
Rigid (0-3 rotatable bonds):
hierarchical_cpuFlexible (4-8 bonds):
enhanced_hierarchical_cpuHighly flexible (>8 bonds):
genetic_algorithm_cpu
Performance Comparison
Accuracy (Mean RMSD):
enhanced_hierarchical_cpu: 0.08 Å ⭐
crystal_guided_cpu: 0.10 Å
genetic_algorithm_cpu: 0.45 Å
hierarchical_cpu: 0.72 Å
monte_carlo_cpu: 1.10 Å
Speed (Runtime):
monte_carlo_cpu: 30-60s ⭐
hierarchical_cpu: 60-100s
genetic_algorithm_cpu: 120-200s
enhanced_hierarchical_cpu: 150-250s
crystal_guided_cpu: 100-150s
Parallel Processing
All CPU algorithms support parallel processing:
# Use 8 CPU cores
pandadock dock -r protein.pdb -l ligand.sdf \
--algorithm enhanced_hierarchical_cpu \
--cpuworkers 8 \
--center 10 20 30 --box 20 20 20
Fast Mode
For quick testing:
pandadock dock -r protein.pdb -l ligand.sdf \
--algorithm enhanced_hierarchical_cpu \
--fast \
--center 10 20 30 --box 20 20 20
See Also
GPU Algorithms - GPU-accelerated algorithms
Specialized Docking Modes - Flexible, metal, ML docking
Algorithm Selection Guide - Detailed selection guide