Diffusion-Guided Molecular Docking Generation

A diffusion-based molecular docking generation method that incorporates DiffGui's "diffusion guidance" principle, introducing differentiable geometric constraints and docking score guidance during the diffusion sampling process.

Core Idea

This work extends DiffGui's diffusion guidance concept from bond prediction to molecular docking:

• DiffGui: Uses a differentiable bond predictor network to guide diffusion sampling
• Our Method: Uses a differentiable docking score predictor network to guide ligand generation

Key Components

1. Geometry Guidance (geometry_guidance.py)

Analytical geometric energy-based guidance that requires no training:

• Pocket center guidance: Encourages ligand atoms to distribute near the pocket center
• Collision penalty: Prevents severe internal atom overlaps
• Gradient-based update: new_pos = pos - step_size * grad(energy)

2. Docking Score Predictor (docking_score_predictor.py)

A lightweight differentiable neural network that approximates Vina docking scores:

• Input: Protein/ligand atom types and 3D coordinates
• Architecture: Atom embeddings + radial basis functions (RBF) + MLP
• Output: Scalar docking score per complex
• Training: Supervised learning on MOAD dataset with Vina scores

3. Docking Guidance Step (docking_score_predictor.py)

Gradient-based guidance following DiffGui's bond_guidance pattern:

ligand_pos_in = pred_pos.detach().clone().requires_grad_(True)
score = predictor(...)
grad = torch.autograd.grad(score.mean(), ligand_pos_in)[0]
new_pos = ligand_pos_in + step_size * grad

4. Two-Stage Guidance Strategy

• Stage A (t > T/2): Geometry guidance for fast pocket localization
• Stage B (t ≤ T/2): Docking score guidance for fine-tuning binding modes

File Structure

GitHub/
├── README.md                    # Documentation
├── requirements.txt              # Python dependencies
├── geometry_guidance.py          # Geometry guidance module
├── docking_score_predictor.py    # Docking score predictor and guidance
├── docking_guided_model.py      # Main model integrating guidance
├── compare_baselines.py          # Baseline comparison script
├── mindmap.md                    # Architecture mindmap
└── __init__.py                  # Package initialization

Code Overview

geometry_guidance.py

• _center_and_clash_energy(): Computes geometric energy (pocket center + collision penalty)
• geometry_guidance_step(): Performs one gradient-guided update on ligand coordinates

docking_score_predictor.py

• DockingScorePredictor: Differentiable neural network that approximates Vina docking scores
• docking_guidance_step(): Gradient-based guidance following DiffGui's pattern

docking_guided_model.py

• DockingGuidedDiffusionModel: Main model that integrates geometry and docking guidance
• sample_with_guidance(): Sampling method with two-stage guidance

compare_baselines.py

• BaselineComparator: Compares different guidance methods (no guidance, geometry-only, docking-only, two-stage)

Usage

Basic Usage

from geometry_guidance import geometry_guidance_step
from docking_score_predictor import DockingScorePredictor, docking_guidance_step
from docking_guided_model import DockingGuidedDiffusionModel

# Initialize model
# Note: Provide your own sample_function in docking_config or ensure models.sample.sample_loop3 is available
model = DockingGuidedDiffusionModel(
    base_model=base_diffusion_model,
    docking_config={
        'use_geometry_guidance': True,
        'geometry_guidance_step_size': 1.0,
        'geometry_center_radius': 5.0,
        'sample_function': your_sample_function,  # Optional: provide custom sampling function
        'tmp_dir': './tmp_docking',  # Optional: temporary directory for docking
    },
    use_docking_guidance=True,
)

# Sample with guidance
results = model.sample_with_guidance(
    batch=batch,
    noiser=noiser,
    device='cuda',
    num_samples=20,
    top_k=5,
)

Baseline Comparison

from compare_baselines import BaselineComparator

comparator = BaselineComparator(
    base_model=base_model,
    docking_config=docking_config,
    device='cuda',
    num_samples_per_method=20,
    num_test_proteins=10,
)

summary, detailed_results = comparator.compare_all_methods(test_batches, noiser)
comparator.print_comparison_table(summary)

Key Design Principles

1. Differentiability

All components are differentiable to enable gradient-based guidance:

• Geometry energy function: E_geo(x) with grad(E_geo)
• Docking score predictor: Neural network with grad(score)

2. Real-Time Guidance

Guidance occurs during diffusion sampling, not post-hoc:

• Each time step: x_{t-1} = diffusion_update(x_t) + guidance_delta
• Follows DiffGui's design pattern exactly

3. Two-Stage Strategy

• Geometry guidance: Fast, no training data needed
• Docking guidance: Precise, requires trained predictor
• Combination: Best of both worlds

Experimental Results

Method	Avg Docking Score	Best Score	Success Rate
No Guidance	[Results]	[Results]	[Results]
Geometry Only	[Results]	[Results]	[Results]
Docking Only	[Results]	[Results]	[Results]
Two-Stage (Ours)	[Results]	[Results]	[Results]

Note: Please refer to the paper for detailed experimental results.

Dependencies

Install required packages:

pip install -r requirements.txt

Core dependencies:

• PyTorch >= 1.9.0
• NumPy >= 1.21.0
• Pandas >= 1.3.0
• RDKit (for molecular operations)
• AutoDock Vina (for docking evaluation, install via conda: conda install -c conda-forge vina)

Citation

If you use this code, please cite:

@article{your_paper,
  title={Diffusion-Guided Molecular Docking Generation: Applying DiffGui's Guidance Principle to Structure-Based Drug Design},
  author={Your Name},
  journal={Journal Name},
  year={2024}
}

License

[Your License]

Acknowledgments

• Inspired by DiffGui's diffusion guidance mechanism
• Built on FlashDiff diffusion model framework
• Uses MOAD dataset for training