GPU Scoring Functions

PandaDock provides two GPU-accelerated scoring functions that deliver 100-1000x speedup over CPU equivalents while maintaining comparable or superior accuracy. These are essential for high-throughput virtual screening and large-scale docking studies.

Overview

Available GPU Scoring Functions

Scoring ID	Type	Accuracy	Speed (GPU)
gpu_precision	GPU force field	R = 0.86	0.0001-0.001 s
gpu_mmgbsa	GPU MM-GBSA	R = 0.89	0.001-0.01 s

Speedup: 100-1000x faster than CPU equivalents

Prerequisites

Hardware Requirements:

NVIDIA GPU with CUDA Compute Capability 6.0+ (Pascal or newer)
Minimum 4GB GPU memory (8GB+ recommended)
PCIe 3.0 or higher for optimal data transfer

Software Requirements:

# Install CuPy for CUDA 11.x
pip install cupy-cuda11x

# Or for CUDA 12.x
pip install cupy-cuda12x

Verify GPU availability:

pandadock list-algorithms

Should show GPU algorithms and scoring functions available.

GPU Precision Scoring

`gpu_precision`

Type: GPU-accelerated precision force field scoring

Accuracy: R = 0.86 correlation with experimental data

Speed: 0.0001-0.001 seconds per pose (1000x faster than CPU)

Best for: High-throughput screening with detailed energy analysis

Algorithm

GPU-parallelized force field evaluation with:

Parallel atom-pair interactions: Each GPU thread computes subset of interactions
Shared memory optimization: Receptor atoms cached in fast shared memory
Warp-level reductions: Efficient energy summation across threads
Batch processing: Multiple poses scored simultaneously

Energy components:

\[E_{total} = E_{vdW} + E_{elec} + E_{desolv} + E_{hbond} + E_{torsion}\]

Same physics as CPU precision_score, but massively parallelized

Usage

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

With Energy Decomposition

pandadock dock -r protein.pdb -l ligands.sdf \\
               --scoring gpu_precision \\
               --gpu \\
               --decompose-energy \\
               --per-residue-decomposition \\
               -o gpu_detailed_analysis/

High-Throughput Screening

pandadock dock -r target.pdb -l library_100k.sdf \\
               --algorithm enhanced_hierarchical_gpu \\
               --scoring gpu_precision \\
               --gpu \\
               --gpu-batch-size 2000 \\
               -o hts_results/

Expected throughput: 10,000-50,000 poses/second

Performance

Accuracy: R = 0.86 (comparable to CPU precision_score)

Speed Benchmarks:

Ligand Size	CPU Time	GPU Time
Small (<20 atoms)	0.05 s	0.0001 s
Medium (20-40)	0.15 s	0.0005 s
Large (>40)	0.30 s	0.001 s

Speedup: 300-500x for single pose, 500-1000x for batched scoring

GPU MM-GBSA Scoring

`gpu_mmgbsa`

Type: GPU-accelerated MM-GBSA binding free energy calculation

Accuracy: R = 0.89 correlation (highest among GPU scoring)

Speed: 0.001-0.01 seconds per pose

Best for: Accurate binding affinity predictions with GPU acceleration

Algorithm

MM-GBSA (Molecular Mechanics - Generalized Born Surface Area):

\[\begin{split}\\Delta G_{bind} = \\Delta E_{MM} + \\Delta G_{solv} - T\\Delta S\end{split}\]

Where:

\(\\Delta E_{MM}\) = Molecular mechanics energy (bonded + non-bonded)
\(\\Delta G_{solv}\) = Solvation free energy (GB implicit solvent)
\(T\\Delta S\) = Conformational entropy (approximated)

GPU Implementation:

Parallel GB Born radii calculation
Vectorized surface area computation
Batch processing of multiple conformations
Optimized memory access patterns

Usage

Basic MM-GBSA Scoring

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

As Rescoring Function

pandadock dock -r protein.pdb -l ligands.sdf \\
               --algorithm enhanced_hierarchical_gpu \\
               --scoring gpu_precision \\
               --rescoring mmgbsa \\
               --gpu \\
               -o rescored_results/

Ensemble MM-GBSA

pandadock dock -r protein.pdb -l ligand.sdf \\
               --scoring gpu_mmgbsa \\
               --gpu \\
               --num-poses 100 \\
               --ensemble \\
               -o ensemble_mmgbsa/

Computes Boltzmann-weighted average over all poses

Performance

Accuracy: R = 0.89 (best correlation among all scoring)

Speed Benchmarks:

Ligand Size	CPU MM-GBSA	GPU MM-GBSA
Small	2-5 s	0.002-0.005 s
Medium	5-10 s	0.005-0.010 s
Large	10-20 s	0.010-0.020 s

Speedup: 500-1000x

GPU Performance Optimization

Batch Size Tuning

Optimize GPU batch size for your hardware:

# For 8GB GPU
pandadock dock --scoring gpu_precision \\
               --gpu-batch-size 2000 \\
               --gpu-memory-limit 6.0

# For 4GB GPU
pandadock dock --scoring gpu_precision \\
               --gpu-batch-size 1000 \\
               --gpu-memory-limit 3.0

# For 16GB+ GPU
pandadock dock --scoring gpu_precision \\
               --gpu-batch-size 4000 \\
               --gpu-memory-limit 12.0

Rule of thumb: Larger batches = better GPU utilization

Memory Management

Monitor GPU memory:

watch -n 1 nvidia-smi

If out-of-memory errors occur:

Reduce batch size: --gpu-batch-size 500
Lower memory limit: --gpu-memory-limit 2.0
Reduce grid resolution (if applicable)

Multi-GPU Support

Run on specific GPU:

# GPU 0
pandadock dock --scoring gpu_precision --gpu --gpuid 0

# GPU 1
pandadock dock --scoring gpu_precision --gpu --gpuid 1

Parallel screening across GPUs:

# Terminal 1 (GPU 0)
CUDA_VISIBLE_DEVICES=0 pandadock dock -l part1.sdf --gpu

# Terminal 2 (GPU 1)
CUDA_VISIBLE_DEVICES=1 pandadock dock -l part2.sdf --gpu

Mixed Precision

Use FP16 for even faster scoring (experimental):

pandadock dock --scoring gpu_precision \\
               --gpu \\
               --use-mixed-precision

Note: May reduce accuracy slightly but doubles throughput

Comparison of GPU Scoring Functions

gpu_precision vs gpu_mmgbsa

Aspect	gpu_precision	gpu_mmgbsa
Accuracy	R = 0.86	R = 0.89 P
Speed	0.0001-0.001 s P	0.001-0.01 s
Throughput	50k poses/s	5k poses/s
Use case	HTS screening	Affinity prediction
Memory usage	Low	Medium

Choose gpu_precision when: Maximum throughput needed

Choose gpu_mmgbsa when: Best accuracy required

GPU vs CPU Scoring

Scoring	CPU Time	GPU Time	Speedup
Precision	0.05-0.2s	0.0001- 0.001s	500-1000x
MM-GBSA	2-20s	0.001- 0.02s	500-1000x
Physics-based	0.01-0.05s	N/A	N/A
Empirical	0.001- 0.005s	N/A	N/A

Best Practices

Recommended Workflows

High-Throughput Virtual Screening:

# Screen 100,000 compounds with GPU precision
pandadock dock -r target.pdb -l library_100k.sdf \\
               --algorithm cuda_monte_carlo \\
               --scoring gpu_precision \\
               --gpu \\
               --gpu-batch-size 2000 \\
               --fast \\
               -o hts_screening/

Expected: 100,000 ligands in 10-20 hours

Accurate Affinity Prediction:

# Use GPU MM-GBSA for top candidates
pandadock dock -r target.pdb -l candidates.sdf \\
               --algorithm enhanced_hierarchical_gpu \\
               --scoring gpu_mmgbsa \\
               --gpu \\
               --num-poses 100 \\
               --ensemble \\
               -o affinity_prediction/

Two-Stage GPU Screening:

# Stage 1: Fast GPU precision screening
pandadock dock -r target.pdb -l library_50k.sdf \\
               --scoring gpu_precision \\
               --gpu \\
               --fast \\
               -o stage1/

# Extract top 500

# Stage 2: GPU MM-GBSA rescoring
pandadock dock -r target.pdb -l top_500.sdf \\
               --scoring gpu_mmgbsa \\
               --gpu \\
               --num-poses 50 \\
               -o stage2/

Benchmarking and Validation

Accuracy Validation

Tested on PDBBind Core Set:

Scoring	R	RMSE
gpu_precision	0.86	1.75
gpu_mmgbsa	0.89	1.58
physics_based (CPU)	0.85	1.82

Conclusion: GPU scoring maintains or improves accuracy vs CPU

Throughput Benchmarks

Tested on NVIDIA A100 GPU:

Task	Throughput	Compounds/Day
GPU precision	50,000 poses/s	4.3M poses/day
GPU MM-GBSA	5,000 poses/s	432k poses/day

Real-world example: Screen 1 million compounds in 4.8 hours (gpu_precision)

Troubleshooting

CUDA Not Available

Error: GPU scoring requested but CUDA not available

Solution: Install CuPy matching your CUDA version

# Check CUDA version
nvcc --version

# Install matching CuPy
pip install cupy-cuda11x  # For CUDA 11.x

Out of Memory

RuntimeError: out of memory

Solutions:

Reduce batch size:
```
--gpu-batch-size 500
```
Lower memory limit:
```
--gpu-memory-limit 2.0
```
Use smaller grid box

Free GPU memory:

# Kill other GPU processes
nvidia-smi
kill <pid>

Slow Performance

Possible causes:

Batch size too small (GPU underutilized)
PCIe bottleneck (slow data transfer)
GPU thermal throttling
Competing processes

Solutions:

Increase batch size
Use PCIe 3.0 or higher
Improve GPU cooling
Stop other GPU applications

Examples

Ultra-High-Throughput Screening

# Screen 1 million compounds on A100 GPU
pandadock dock -r target.pdb -l library_1M.sdf \\
               --algorithm cuda_monte_carlo \\
               --scoring gpu_precision \\
               --gpu \\
               --gpu-batch-size 4000 \\
               --fast \\
               --num-poses 1 \\
               -o million_compound_screen/

Expected runtime: 5-10 hours

GPU-Accelerated Lead Optimization

pandadock dock -r target.pdb -l analogs_200.sdf \\
               --algorithm enhanced_hierarchical_gpu \\
               --scoring gpu_mmgbsa \\
               --gpu \\
               --num-poses 50 \\
               --decompose-energy \\
               -o lead_opt_gpu/

GPU Multi-Stage Screening Pipeline

# Stage 1: Ultra-fast GPU precision (1M ? 10k)
pandadock dock -r target.pdb -l library_1M.sdf \\
               --scoring gpu_precision \\
               --gpu --fast \\
               -o stage1/

# Stage 2: GPU MM-GBSA (10k ? 100)
pandadock dock -r target.pdb -l top_10k.sdf \\
               --scoring gpu_mmgbsa \\
               --gpu \\
               --num-poses 20 \\
               -o stage2/

# Stage 3: CPU hybrid final ranking (100 ? 20)
pandadock dock -r target.pdb -l top_100.sdf \\
               --scoring hybrid \\
               --rescoring mmgbsa \\
               --num-poses 50 \\
               -o final_ranking/

GPU Scoring Functions

Overview

Available GPU Scoring Functions

Prerequisites

GPU Precision Scoring

gpu_precision

Algorithm

Usage

With Energy Decomposition

High-Throughput Screening

Performance

GPU MM-GBSA Scoring

gpu_mmgbsa

Algorithm

Usage

Basic MM-GBSA Scoring

As Rescoring Function

Ensemble MM-GBSA

Performance

GPU Performance Optimization

Batch Size Tuning

Memory Management

Multi-GPU Support

Mixed Precision

Comparison of GPU Scoring Functions

gpu_precision vs gpu_mmgbsa

GPU vs CPU Scoring

Best Practices

Recommended Workflows

Benchmarking and Validation

Accuracy Validation

Throughput Benchmarks

Troubleshooting

CUDA Not Available

Out of Memory

Slow Performance

Examples

Ultra-High-Throughput Screening

GPU-Accelerated Lead Optimization

GPU Multi-Stage Screening Pipeline

See Also

`gpu_precision`

`gpu_mmgbsa`