diffdock

What this skill does

# 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. It represents the state-of-the-art in computational docking, crucial for structure-based drug discovery and chemical biology.

**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

## 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:**
```
results/single_docking/
├── rank_1.sdf          # Top-ranked pose
├── rank_2.sdf          # Second-ranked pose
├── ...
├── rank_10.sdf         # 10th pose (default: 10 samples)
└── confidence_scores.txt
```

### 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):**

Pre-compute protein embeddings for faster processing:
```bash
# Pre-compute embeddings
python datasets/esm_embedding_preparation.py \
  --protein_ligand_csv screening_input.csv \
  --out_file protein_embeddings.pt

# Run with pre-computed embeddings
python -m inference \
  --config default_inference_args.yaml \
  --protein_ligand_csv screening_input.csv \
  --esm_embeddings_path protein_embeddings.pt \
  --out_dir results/screening/
```

### 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