diffdock

diffdock

Overview

DiffDock uses a diffusion generative model to predict protein-ligand binding poses directly from protein structure and ligand SMILES, treating docking as a generative rather than a search problem. Unlike traditional docking tools (AutoDock Vina, Glide), DiffDock does not require a predefined binding site — it samples poses across the full protein surface. It outputs a ranked set of binding poses with associated confidence scores. DiffDock excels at blind docking tasks and produces diverse pose hypotheses, making it valuable for de novo binding site discovery and challenging targets.

When to Use

• Blind docking (unknown binding site): You do not know where on the protein the ligand binds and want to discover candidate binding sites.
• Challenging targets that fail traditional docking: Allosteric sites, flexible regions, or proteins without a co-crystal structure in the target binding site.
• Exploring multiple binding modes: Generating a diverse ensemble of poses to understand conformational flexibility in the binding event.
• Structure-activity relationship (SAR) exploration: Rapidly docking a series of analogs to compare predicted binding modes.
• Fragment screening hypothesis generation: Identifying plausible binding sites for fragment molecules.
• For known binding sites with rigid protein assumptions, AutoDock Vina or GNINA may be faster and equally accurate.
• For large-scale virtual screening (>10,000 compounds), consider GNINA or DiffDock-L (the large-scale version) rather than standard DiffDock.
• Use AutoDock Vina instead when the binding pocket is well-defined and faster throughput is needed for large compound libraries

Prerequisites

• Python packages: diffdock (conda install recommended), rdkit, torch, biopython, nglview (visualization)
• System: GPU strongly recommended (NVIDIA CUDA); CPU inference is slow (~5-10 min/compound)
• Data requirements: Protein PDB file (cleaned, protonated), ligand as SMILES string or SDF file
• Environment: conda environment with CUDA-compatible PyTorch

# Recommended: clone and install from source
git clone https://github.com/gcorso/DiffDock.git
cd DiffDock
conda create -n diffdock python=3.9
conda activate diffdock
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

# Download pretrained model weights
python -c "from utils.download import download_pretrained; download_pretrained()"

Workflow

Step 1: Prepare the Protein Structure