binding-affinity

Binding Affinity Prediction

Overview

This skill predicts protein-ligand binding affinity from docked poses — converting structural information into estimated ΔG (kcal/mol), pKd, and Kd (nM). It complements the molecular-docking skill's interaction analysis (score.py) which counts contacts but does NOT predict binding strength in energy units.

Key capabilities:

• Empirical scoring: descriptor + contact-based affinity prediction using RDKit and BioPython
• MM/GBSA rescoring: physics-based energy decomposition with OpenMM (or RDKit fallback)
• Consensus scoring: combine multiple scoring methods with rank-based normalization
• Batch virtual screening: efficiently score large compound libraries

Validation Warning

All predictions from this skill are computational estimates, NOT experimentally validated measurements.

• Empirical scoring (predict.py): typical error is 1-2 log units pKd (~10-100x in Kd)
• MM/GBSA rescoring: useful for relative ranking, not absolute binding energies
• Use for prioritizing compounds for experimental testing, not for making clinical claims

The scripts include uncertainty ranges and confidence flags to help calibrate expectations.

Output Integrity

Script outputs are RAW computational estimates. The agent MUST NOT:

• Apply "calibration" or scaling to raw pKd/Kd values
• Adjust values to match known experimental data
• Add fields like "calibrated_pKd" not produced by the script
• Present approximate methods (simplified MM/GBSA) as full implementations

The raw output IS the prediction. Report it exactly as produced.

All script invocations are automatically logged to _script_manifest.jsonl. The critique agent uses this manifest to verify that every number in the final report traces to a real script output.

When to Use This Skill

Use the binding-affinity skill when you need to:

• Predict how tightly a ligand binds to a protein (after docking)
• Rank docked poses by estimated binding affinity
• Rescore poses using physics-based MM/GBSA energy decomposition
• Screen compound libraries for binding potential
• Combine multiple scoring methods into a consensus ranking

Trigger phrases: "predict binding affinity", "estimate Kd", "score binding strength", "rescore with MM/GBSA", "rank compounds by affinity", "virtual screening"

Do NOT use this skill for:

• Finding binding pockets (use pocket-detection)
• Generating docked poses (use molecular-docking)
• Predicting ADMET properties (use admet-prediction)
• Free energy perturbation (requires specialized MD simulations)

Related Skills

• molecular-docking: Generate docked poses first. This skill scores them.
• pocket-detection: Find binding pockets before docking.
• admet-prediction: Filter affinity hits by drug-likeness and safety.

Installation

Required Dependencies

# Core (required for all modes)
pip install rdkit-pypi biopython numpy scipy

Optional Dependencies

# MM/GBSA rescoring (full physics-based path)
pip install openmm openmmforcefields openff-toolkit

# If OpenMM is not available, rescore.py falls back to RDKit MMFF

Quick Verification

python -c "from rdkit import Chem; print('RDKit OK')"
python -c "from Bio.PDB import PDBParser; print('BioPython OK')"
python -c "import numpy; print('NumPy OK')"
python -c "import openmm; print('OpenMM OK')"  # optional

Core Workflows

Workflow 1: Predict Binding Affinity

Score docked poses using the empirical descriptor-based model.

python scripts/predict.py \
    --protein prepared_protein.pdb \
    --poses docking_results/poses.sdf \
    --output affinity.json

Workflow 2: MM/GBSA Rescoring

Physics-based rescoring for more accurate relative ranking.

# Full MM/GBSA (requires OpenMM)
python scripts/rescore.py \
    --protein prepared_protein.pdb \
    --poses poses.sdf \
    --output mmgbsa.json \
    --minimize-steps 100

# RDKit fallback (no OpenMM needed)
python scripts/rescore.py \
    --protein prepared_protein.pdb \
    --poses poses.sdf \
    --output mmgbsa.json

Workflow 3: Consensus Scoring

Combine multiple scoring methods for robust ranking.

python scripts/consensus.py \
    --scores affinity.json mmgbsa.json \
    --docking-scores docking_results/scores.csv \
    --interactions interactions.json \
    --output consensus.json \
    --top-n 10

Workflow 4: Batch Virtual Screening

Screen a compound library against a target.

python scripts/batch.py \
    --protein prepared_protein.pdb \
    --library compounds.sdf \
    --output screening_hits.csv \
    --top-n 50 \
    --threshold 6.0

Workflow 5: Full Pipeline

# 1. Detect pockets
python ../pocket-detection/scripts/detect.py \
    --input protein.pdb --output pockets.json

# 2. Dock ligand
python ../molecular-docking/scripts/dock.py \
    --protein protein.pdb --ligand ligand.sdf \
    --output-dir dock_results/ --method vina \
    --center_x 10 --center_y 20 --center_z 15

# 3. Interaction analysis
python ../molecular-docking/scripts/score.py \
    --protein protein.pdb --poses dock_results/poses.sdf \
    --output interactions.json

# 4. Predict affinity
python scripts/predict.py \
    --protein protein.pdb --poses dock_results/poses.sdf \
    --output affinity.json

# 5. MM/GBSA rescore
python scripts/rescore.py \
    --protein protein.pdb --poses dock_results/poses.sdf \
    --output mmgbsa.json

# 6. Consensus
python scripts/consensus.py \
    --scores affinity.json mmgbsa.json \
    --docking-scores dock_results/scores.csv \
    --interactions interactions.json \
    --output final_ranking.json --top-n 5

Script Reference

Output Format

Affinity JSON (from predict.py)

{
  "protein": "protein.pdb",
  "method": "descriptor",
  "n_poses": 5,
  "note": "Empirical estimate. Typical error: 1-2 log units pKd (~10-100x in Kd). Use for relative ranking only.",
  "predictions": [
    {
      "pose_id": 1,
      "pose_name": "ligand_pose_1",
      "predicted_pKd": 7.2,
      "pKd_uncertainty": 1.5,
      "pKd_range": [5.7, 8.7],
      "predicted_dG_kcal": -9.8,
      "predicted_Kd_nM": 60,
      "confidence": "moderate",
      "features": {
        "mw": 342.4,
        "logp": 2.1,
        "n_hbonds": 4,
        "n_hydrophobic": 12,
        "burial_fraction": 0.65
      }
    }
  ]
}

Consensus JSON (from consensus.py)

{
  "n_poses": 5,
  "sources": ["predict.py", "rescore.py", "dock.py", "score.py"],
  "agreement_tau": 0.72,
  "agreement_class": "high",
  "rankings": [
    {
      "pose_id": 1,
      "pose_name": "ligand_pose_1",
      "consensus_score": 0.85,
      "consensus_rank": 1,
      "individual_ranks": {"predict": 1, "rescore": 2, "docking": 1, "interactions": 3}
    }
  ]
}

Output Interpretation

pKd Values

Critical: These are computational estimates with ~1-2 log unit uncertainty. A predicted pKd of 7.2 means the true value is likely somewhere between 5.7 and 8.7 (Kd between ~2 nM and 2 uM).

Confidence Levels

MM/GBSA Energies

• More negative = stronger predicted binding
• Useful for relative ranking within a series, not absolute binding energies
• ΔG_MMGBSA does NOT equal experimental ΔG_binding (missing entropy, sampling)

Troubleshooting

• All poses get similar scores: The ligands may be too similar, or the scoring function may not discriminate well for this target class.
• Negative confidence: Check if molecules are drug-like (MW 200-600, LogP -1 to 5). Non-drug-like molecules get unreliable scores.
• OpenMM not available: rescore.py falls back to RDKit MMFF energies. Install OpenMM for better physics-based scoring.
• Very large library (>10K molecules): Use batch.py with --threshold to filter early.

References

• Wang, R. et al. "The PDBbind database." J. Med. Chem. 47, 2977-2980 (2004).
• Ballester, P.J. & Mitchell, J.B.O. "A machine learning approach to predicting protein-ligand binding affinity." Bioinformatics 26, 1169-1175 (2010).
• Hou, T. et al. "Assessing the performance of the MM/PBSA and MM/GBSA methods." J. Chem. Inf. Model. 51, 69-82 (2011).
• Li, H. et al. "Improving AutoDock Vina Using Random Forest." J. Chem. Inf. Model. 55, 1291-1299 (2015).