Agent Skills
› synthetic-sciences/openscience
› binding-affinity
binding-affinity
GitHub基于混合ML与物理方法的蛋白质-配体结合亲和力预测技能。支持经验打分、MM/GBSA重评分、多方法共识排序及批量虚拟筛选，将结构信息转化为ΔG、pKd和Kd估算值，用于化合物优先级排序。
Trigger Scenarios
预测结合亲和力
估算Kd
评估结合强度
使用MM/GBSA重评分
按亲和力对化合物排序
虚拟筛选
Install
npx skills add synthetic-sciences/openscience --skill binding-affinity -g -y
SKILL.md
Frontmatter
{
"name": "binding-affinity",
"tags": [
"Binding Affinity",
"Drug Discovery",
"Scoring",
"MM\/GBSA",
"Virtual Screening"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "chemistry",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"description": "Hybrid ML + physics binding affinity prediction. Empirical scoring, MM\/GBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.",
"dependencies": [
"rdkit-pypi",
"biopython>=1.84",
"numpy",
"scipy"
]
}
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
| Script | Purpose | Key Inputs | Key Outputs |
|---|---|---|---|
scripts/predict.py |
Empirical affinity prediction | Protein PDB + Poses SDF | Affinity JSON |
scripts/rescore.py |
MM/GBSA rescoring | Protein PDB + Poses SDF | Energy JSON |
scripts/consensus.py |
Multi-method consensus | Multiple score JSONs | Consensus JSON |
scripts/batch.py |
Batch virtual screening | Protein PDB + Library SDF | Hits CSV |
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
| pKd | Kd (approx) | Interpretation |
|---|---|---|
| > 9 | < 1 nM | Very potent (clinical candidate range) |
| 7-9 | 1-100 nM | Potent (lead compound range) |
| 5-7 | 100 nM - 10 uM | Moderate (hit range) |
| 3-5 | 10 uM - 10 mM | Weak (fragment range) |
| < 3 | > 10 mM | Very weak / non-binder |
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
| Level | Criteria | Meaning |
|---|---|---|
| High | MW 200-600, LogP -1 to 5, >30 contacts | Within training domain, estimate more reliable |
| Moderate | Partially within domain | Use with caution |
| Low | MW <200 or >600, extreme LogP, few contacts | Outside training domain, estimate unreliable |
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
--thresholdto 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).
Version History
- e9844a4 Current 2026-07-11 17:21
Dependencies
-
required
rdkit-pypi -
required
biopython>=1.84 -
required
numpy -
required
scipy
