Alterlab Diffdock

---
name: alterlab-diffdock
description: Predicts protein-ligand binding poses with DiffDock diffusion-based molecular docking from PDB structures and SMILES, producing pose confidence scores for virtual screening and structure-based drug design. Use when docking ligands into a protein, generating binding poses, or screening compounds against a target; not for binding affinity prediction. Part of the AlterLab Academic Skills suite.
license: MIT
allowed-tools: Read Write Edit Bash(python:*) Bash(uv:*)
compatibility: "Requires a local DiffDock checkout (gcorso/DiffDock via conda or Docker) and a GPU for practical use; no API key or account. The skill's helper scripts (scripts/) run standalone under python/uv."
metadata:
    skill-author: AlterLab
    version: "1.0.1"
    last_updated: "2026-09-23"
---

# DiffDock: Molecular Docking with Diffusion Models

## Overview

DiffDock is a diffusion-based deep learning tool for molecular docking that predicts 3D binding poses of small molecule ligands to protein targets. The repository's default model is DiffDock-L (Feb 2024); it is widely used for blind docking, though co-folding models (Boltz-2, Chai-1) and physics-based rescoring are now common complements, and predicted poses should be checked for physical validity (e.g. with PoseBusters).

**Core Capabilities:**
- Predict ligand binding poses with high accuracy using deep learning
- Support protein structures (PDB files) or sequences (via ESMFold)
- Process single complexes or batch virtual screening campaigns
- Generate confidence scores to assess prediction reliability
- Handle diverse ligand inputs (SMILES, SDF, MOL2)

**Key Distinction:** DiffDock predicts **binding poses** (3D structure) and **confidence** (prediction certainty), NOT binding affinity (ΔG, Kd). Always combine with scoring functions (GNINA, MM/GBSA) for affinity assessment.

## When to Use This Skill

This skill should be used when:

- "Dock this ligand to a protein" or "predict binding pose"
- "Run molecular docking" or "perform protein-ligand docking"
- "Virtual screening" or "screen compound library"
- "Where does this molecule bind?" or "predict binding site"
- Structure-based drug design or lead optimization tasks
- Tasks involving PDB files + SMILES strings or ligand structures
- Batch docking of multiple protein-ligand pairs

### Does NOT Trigger

| Scenario | Use Instead |
|----------|-------------|
| Binding affinity / docking score on a cloud platform (AutoDock Vina) | `alterlab-rowan` |
| MD simulation of a complex, pose-stability / RMSD over a trajectory | `alterlab-molecular-dynamics` |
| Protein–protein or protein–nucleic-acid complex structure prediction | `alterlab-boltz` or `alterlab-chai` |
| Predicting the apo protein structure itself (no ligand) | `alterlab-alphafold` |
| Looking up measured protein–ligand affinities for the target | `alterlab-bindingdb` |

## Installation and Environment Setup

### Check Environment Status

Before proceeding with DiffDock tasks, verify the environment setup:

```bash
# Use the provided setup checker
python scripts/setup_check.py
```

This script validates Python version, PyTorch with CUDA, PyTorch Geometric, RDKit, ESM, and other dependencies.

### Installation Options

**Option 1: Conda (Recommended)**
```bash
git clone https://github.com/gcorso/DiffDock.git
cd DiffDock
conda env create --file environment.yml
conda activate diffdock
```

**Option 2: Docker**
```bash
docker pull rbgcsail/diffdock
docker run -it --gpus all --entrypoint /bin/bash rbgcsail/diffdock
micromamba activate diffdock
```

**Important Notes:**
- GPU strongly recommended (10-100x speedup vs CPU)
- First run pre-computes SO(2)/SO(3) lookup tables (~2-5 minutes)
- Model checkpoints (~500MB) download automatically if not present

## Core Workflows

### Workflow 1: Single Protein-Ligand Docking

**Use Case:** Dock one ligand to one protein target

**Input Requirements:**
- Protein: PDB file OR amino acid sequence
- Ligand: SMILES string OR structure file (SDF/MOL2)

**Command:**
```bash
python -m inference \
  --config default_inference_args.yaml \
  --protein_path protein.pdb \
  --ligand "CC(=O)Oc1ccccc1C(=O)O" \
  --out_dir results/single_docking/
```

**Alternative (protein sequence):**
```bash
python -m inference \
  --config default_inference_args.yaml \
  --protein_sequence "MSKGEELFTGVVPILVELDGDVNGHKF..." \
  --ligand ligand.sdf \
  --out_dir results/sequence_docking/
```

**Output Structure:** DiffDock writes one subdirectory per complex (even for a single run), with the confidence embedded in each pose's filename:
```
results/single_docking/
└── complex_0/
    ├── rank1.sdf                   # Top pose (no score in name)
    ├── rank1_confidence-0.42.sdf   # Same pose, confidence in filename
    ├── rank2_confidence-1.10.sdf   # 2nd-ranked pose
    ├── ...
    └── rank10_confidence-3.05.sdf  # 10th pose (default: 10 samples)
```
There is no separate `confidence_scores.txt`; the confidence value is the `confidenceN.NN` suffix. Poses are ranked by descending confidence (`rank1` = best).

### Workflow 2: Batch Processing Multiple Complexes

**Use Case:** Dock multiple ligands to proteins, virtual screening campaigns

**Step 1: Prepare Batch CSV**

Use the provided script to create or validate batch input:

```bash
# Create template
python scripts/prepare_batch_csv.py --create --output batch_input.csv

# Validate existing CSV
python scripts/prepare_batch_csv.py my_input.csv --validate
```

**CSV Format:**
```csv
complex_name,protein_path,ligand_description,protein_sequence
complex1,protein1.pdb,CC(=O)Oc1ccccc1C(=O)O,
complex2,,COc1ccc(C#N)cc1,MSKGEELFT...
complex3,protein3.pdb,ligand3.sdf,
```

**Required Columns:**
- `complex_name`: Unique identifier
- `protein_path`: PDB file path (leave empty if using sequence)
- `ligand_description`: SMILES string or ligand file path
- `protein_sequence`: Amino acid sequence (leave empty if using PDB)

**Step 2: Run Batch Docking**

```bash
python -m inference \
  --config default_inference_args.yaml \
  --protein_ligand_csv batch_input.csv \
  --out_dir results/batch/ \
  --batch_size 10
```

**For Large Virtual Screening (>100 compounds):**

Put every protein–ligand pair in one CSV and run a single `inference` call. At start-up
`inference.py` computes the ESM2 (`esm2_t33_650M_UR50D`) embeddings for all complexes in the
CSV in one batched pass and reuses them for the confidence model — there is no separate
pre-computation step and no `--esm_embeddings_path` flag for inference.
(`datasets/esm_embedding_preparation.py` / `esm_embeddings_to_pt.py` exist only for the
training/evaluation benchmarks.) Split very large libraries into several CSVs if GPU memory
or run time is a concern.

**Config gotcha:** values in the `--config` YAML are applied *after* the CLI flags and
override them. `samples_per_complex`, `inference_steps`, and the `temp_*` parameters are in
`default_inference_args.yaml`, so passing e.g. `--samples_per_complex 20` alongside
`--config default_inference_args.yaml` has no effect — edit a copy of the YAML instead
(`--batch_size` is not in the default YAML, so the CLI flag works).

### Workflow 3: Analyzing Results

After docking completes, analyze confidence scores and rank predictions:

```bash
# Analyze all results
python scripts/analyze_results.py results/batch/

# Show top 5 per complex
python scripts/analyze_results.py results/batch/ --top 5

# Filter by confidence threshold
python scripts/analyze_results.py results/batch/ --threshold 0.0

# Export to CSV
python scripts/analyze_results.py results/batch/ --export summary.csv

# Show top 20 predictions across all complexes
python scripts/analyze_results.py results/batch/ --best 20
```

The analysis script:
- Parses confidence scores from all predictions
- Classifies as High (>0), Moderate (-1.5 to 0), or Low (<-1.5)
- Ranks predictions within and across complexes
- Generates statistical summaries
- Exports results to CSV for downstream analysis

## Confidence Score Interpretation

**Understanding Scores:**

| Score Range | Confidence Level | Interpretation |
|------------|------------------|----------------|
| **> 0** | High | Strong prediction, likely accurate |
| **-1.5 to 0** | Moderate | Reasonable prediction, validate carefully |
| **< -1.5** | Low | Uncertain prediction, requires validation |

**Critical Notes:**
1. **Confidence ≠ Affinity**: High confidence means model certainty about structure, NOT strong binding
2. **Context Matters**: Adjust expectations for:
   - Large ligands (>500 Da): Lower confidence expected
   - Multiple protein chains: May decrease confidence
   - Novel protein families: May underperform
3. **Multiple Samples**: Review top 3-5 predictions, look for consensus

**For detailed guidance:** Read `references/confidence_and_limitations.md` using the Read tool

## Parameter Customization

### Using Custom Configuration

Create custom configuration for specific use cases:

```bash
# Copy template
cp assets/custom_inference_config.yaml my_config.yaml

# Edit parameters (see template for presets)
# Then run with custom config
python -m inference \
  --config my_config.yaml \
  --protein_ligand_csv input.csv \
  --out_dir results/
```

### Key Parameters to Adjust

**Sampling Density:**
- `samples_per_complex: 10` → Increase to 20-40 for difficult cases
- More samples = better coverage but longer runtime

**Inference Steps:**
- `inference_steps: 20` → Increase to 25-30 for higher accuracy
- More steps = potentially better quality but slower

**Temperature Parameters (control diversity):**
- `temp_sampling_tor: 7.04` → Increase for flexible ligands (8-10)
- `temp_sampling_tor: 7.04` → Decrease for rigid ligands (5-6)
- Higher temperature = more diverse poses

**Presets Available in Template:**
1. High Accuracy: More samples + steps, lower temperature
2. Fast Screening: Fewer samples, faster
3. Flexible Ligands: Increased torsion temperature
4. Rigid Ligands: Decreased torsion temperature

**For complete parameter reference:** Read `references/parameters_reference.md` using the Read tool

## Advanced Techniques

### Ensemble Docking (Protein Flexibility)

For proteins with known flexibility, dock to multiple conformations:

```python
# Create ensemble CSV
import pandas as pd

conformations = ["conf1.pdb", "conf2.pdb", "conf3.pdb"]
ligand = "CC(=O)Oc1ccccc1C(=O)O"

data = {
    "complex_name": [f"ensemble_{i}" for i in range(len(conformations))],
    "protein_path": conformations,
    "ligand_description": [ligand] * len(conformations),
    "protein_sequence": [""] * len(conformations)
}

pd.DataFrame(data).to_csv("ensemble_input.csv", index=False)
```

Run docking with increased sampling (set `samples_per_complex: 20` in a copy of the YAML —
a `--samples_per_complex` flag would be overwritten by the default config):
```bash
cp default_inference_args.yaml ensemble_args.yaml   # edit: samples_per_complex: 20
python -m inference \
  --config ensemble_args.yaml \
  --protein_ligand_csv ensemble_input.csv \
  --out_dir results/ensemble/
```

### Integration with Scoring Functions

DiffDock generates poses; combine with other tools for affinity:

**GNINA (Fast neural network scoring):**
```bash
for pose in results/*.sdf; do
    gnina -r protein.pdb -l "$pose" --score_only
done
```

**MM/GBSA (More accurate, slower):**
Use AmberTools MMPBSA.py or gmx_MMPBSA after energy minimization

**Free Energy Calculations (Most accurate):**
Use OpenMM + OpenFE or GROMACS for FEP/TI calculations

**Recommended Workflow:**
1. DiffDock → Generate poses with confidence scores
2. Visual inspection → Check structural plausibility
3. GNINA or MM/GBSA → Rescore and rank by affinity
4. Experimental validation → Biochemical assays

## Limitations and Scope

**DiffDock IS Designed For:**
- Small molecule ligands (typically 100-1000 Da)
- Drug-like organic compounds
- Small peptides (<20 residues)
- Single or multi-chain proteins

**DiffDock IS NOT Designed For:**
- Large biomolecules (protein-protein docking) → Use DiffDock-PP or AlphaFold-Multimer
- Large peptides (>20 residues) → Use alternative methods
- Covalent docking → Use specialized covalent docking tools
- Binding affinity prediction → Combine with scoring functions
- Membrane proteins → Not specifically trained, use with caution

**For complete limitations:** Read `references/confidence_and_limitations.md` using the Read tool

## Troubleshooting

### Common Issues

**Issue: Low confidence scores across all predictions**
- Cause: Large/unusual ligands, unclear binding site, protein flexibility
- Solution: Increase `samples_per_complex` (20-40), try ensemble docking, validate protein structure

**Issue: Out of memory errors**
- Cause: GPU memory insufficient for batch size
- Solution: Reduce `--batch_size 2` or process fewer complexes at once

**Issue: Slow performance**
- Cause: Running on CPU instead of GPU
- Solution: Verify CUDA with `python -c "import torch; print(torch.cuda.is_available())"`, use GPU

**Issue: Unrealistic binding poses**
- Cause: Poor protein preparation, ligand too large, wrong binding site
- Solution: Check protein for missing residues, remove far waters, consider specifying binding site

**Issue: "Module not found" errors**
- Cause: Missing dependencies or wrong environment
- Solution: Run `python scripts/setup_check.py` to diagnose

### Performance Optimization

**For Best Results:**
1. Use GPU (essential for practical use)
2. Pre-compute ESM embeddings for repeated protein use
3. Batch process multiple complexes together
4. Start with default parameters, then tune if needed
5. Validate protein structures (resolve missing residues)
6. Use canonical SMILES for ligands

## Graphical User Interface

For interactive use, launch the web interface:

```bash
python app/main.py
# Navigate to http://localhost:7860
```

Or try the online demo (https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web),
though the Space is often down — prefer a local or Docker install for reliable runs.

## Resources

### Helper Scripts (`scripts/`)

**`prepare_batch_csv.py`**: Create and validate batch input CSV files
- Create templates with example entries
- Validate file paths and SMILES strings
- Check for required columns and format issues

**`analyze_results.py`**: Analyze confidence scores and rank predictions
- Parse results from single or batch runs
- Generate statistical summaries
- Export to CSV for downstream analysis
- Identify top predictions across complexes

**`setup_check.py`**: Verify DiffDock environment setup
- Check Python version and dependencies
- Verify PyTorch and CUDA availability
- Test RDKit and PyTorch Geometric installation
- Provide installation instructions if needed

### Reference Documentation (`references/`)

**`parameters_reference.md`**: Complete parameter documentation
- All command-line options and configuration parameters
- Default values and acceptable ranges
- Temperature parameters for controlling diversity
- Model checkpoint locations and version flags

Read this file when users need:
- Detailed parameter explanations
- Fine-tuning guidance for specific systems
- Alternative sampling strategies

**`confidence_and_limitations.md`**: Confidence score interpretation and tool limitations
- Detailed confidence score interpretation
- When to trust predictions
- Scope and limitations of DiffDock
- Integration with complementary tools
- Troubleshooting prediction quality

Read this file when users need:
- Help interpreting confidence scores
- Understanding when NOT to use DiffDock
- Guidance on combining with other tools
- Validation strategies

**`workflows_examples.md`**: Comprehensive workflow examples
- Detailed installation instructions
- Step-by-step examples for all workflows
- Advanced integration patterns
- Troubleshooting common issues
- Best practices and optimization tips

Read this file when users need:
- Complete workflow examples with code
- Integration with GNINA, OpenMM, or other tools
- Virtual screening workflows
- Ensemble docking procedures

### Assets (`assets/`)

**`batch_template.csv`**: Template for batch processing
- Pre-formatted CSV with required columns
- Example entries showing different input types
- Ready to customize with actual data

**`custom_inference_config.yaml`**: Configuration template
- Annotated YAML with all parameters
- Four preset configurations for common use cases
- Detailed comments explaining each parameter
- Ready to customize and use

## Best Practices

1. **Always verify environment** with `setup_check.py` before starting large jobs
2. **Validate batch CSVs** with `prepare_batch_csv.py` to catch errors early
3. **Start with defaults** then tune parameters based on system-specific needs
4. **Generate multiple samples** (10-40) for robust predictions
5. **Visual inspection** of top poses before downstream analysis
6. **Combine with scoring** functions for affinity assessment
7. **Use confidence scores** for initial ranking, not final decisions
8. **Pre-compute embeddings** for virtual screening campaigns
9. **Document parameters** used for reproducibility
10. **Validate results** experimentally when possible

## Citations

When using DiffDock, cite the appropriate papers:

**DiffDock-L (current default model):**
```
Corso et al. (2024) "Deep Confident Steps to New Pockets: Strategies for Docking Generalization"
ICLR 2024, arXiv:2402.18396
```

**Original DiffDock:**
```
Corso et al. (2023) "DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking"
ICLR 2023, arXiv:2210.01776
```

## Additional Resources

- **GitHub Repository**: https://github.com/gcorso/DiffDock
- **Online Demo**: https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web
- **DiffDock-L Paper**: https://arxiv.org/abs/2402.18396
- **Original Paper**: https://arxiv.org/abs/2210.01776

Part of the AlterLab Academic Skills suite.