binding-affinity

name	binding-affinity
description	Hybrid ML + physics binding affinity prediction. Empirical scoring, MM/GBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.
category	chemistry
license	MIT
metadata	{"skill-author":"Synthetic Sciences"}
version	1.0.0
author	Synthetic Sciences
tags	["Binding Affinity","Drug Discovery","Scoring","MM/GBSA","Virtual Screening"]
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