Scoring Functions Overview

PandaDock provides 6 advanced scoring functions for evaluating protein-ligand binding energies.

Available Scoring Functions

physics_based

Comprehensive force field scoring with van der Waals, electrostatics, desolvation, hydrogen bonding, and torsional penalties. Recommended for general docking.

Accuracy: R = 0.85 correlation with experimental data
Speed: 0.01-0.05 seconds per pose
Best for: General purpose docking, balanced accuracy

empirical

Fast empirical scoring based on statistical potentials from protein-ligand databases.

Accuracy: R = 0.72 correlation
Speed: 0.001-0.005 seconds per pose (fastest)
Best for: Virtual screening, initial filtering

precision_score

High-precision interaction energy decomposition with per-residue contributions.

Accuracy: R = 0.87 correlation
Speed: 0.05-0.2 seconds per pose
Best for: Detailed interaction analysis, lead optimization

hybrid

Combined physics-based and machine learning scoring for maximum accuracy.

Accuracy: R = 0.91 correlation (best)
Speed: 0.1-0.3 seconds per pose
Best for: Critical predictions, final ranking

gpu_precision

GPU-accelerated precision scoring for large-scale studies.

Accuracy: R = 0.86 correlation
Speed: 0.0001-0.001 seconds per pose (GPU)
Best for: High-throughput screening with GPU

gpu_mmgbsa

GPU-accelerated MM-GBSA rescoring for binding free energies.

Accuracy: R = 0.89 correlation
Speed: 0.001-0.01 seconds per pose (GPU)
Best for: Accurate binding affinity predictions

Usage Examples

Basic Usage

pandadock dock -r protein.pdb -l ligand.sdf \
               --scoring physics_based \
               --center 10 20 30 --box 20 20 20

High Accuracy

pandadock dock -r protein.pdb -l ligand.sdf \
               --scoring hybrid \
               --center 10 20 30 --box 20 20 20

Fast Screening

pandadock dock -r protein.pdb -l library.sdf \
               --scoring empirical \
               --center 10 20 30 --box 20 20 20

GPU Accelerated

pandadock dock -r protein.pdb -l ligand.sdf \
               --scoring gpu_precision \
               --gpu \
               --center 10 20 30 --box 20 20 20

Selection Guide

Use Case	Scoring	Speed	Accuracy
General docking	physics_ based	Medium	High (R=0.85)
Virtual screening	empirical	Fast	Medium (R=0.72)
Lead optimization	hybrid	Slow	Highest (R=0.91)
Detailed analysis	precision _score	Medium- Slow	High (R=0.87)
GPU high- throughput	gpu_ precision	Very Fast	High (R=0.86)