---

name: binding-affinity description: "Plan DGL-LifeSci protein-ligand binding affinity workflows with PDBBind, local complexes, ACNN, PotentialNet, complex graph construction, and safe input checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Binding Affinity

Use this sub-skill when a task involves protein-ligand binding affinity prediction with DGL-LifeSci structure-based workflows.

Use This For

• Planning PDBBind core or refined workflows without immediately downloading or training on the full dataset.
• Preparing local protein-ligand complexes with ligand files, protein/pocket PDB files, 3D coordinates, and load_molecule flags.
• Choosing between ACNN and PotentialNet and matching each model to the graph object it expects.
• Constructing complex graphs with ACNN_graph_construction_and_featurization or PN_graph_construction_and_featurization.
• Running safe, local smoke checks on protein/ligand file paths before launching graph construction or training.

Start Here

1. Read references/data-formats.md to validate protein/ligand file formats, PDBBind-style layout, coordinate expectations, and load_molecule flags.
2. Run scripts/check_complex_inputs.py --help, then check the user's local complex pair before constructing graphs.
3. Read references/workflows.md to pick ACNN or PotentialNet, build the PDBBind/local-complex plan, and map graph outputs into model inputs.
4. Use references/troubleshooting.md when imports, RDKit parsing, missing conformations, PDBBind paths, config choices, or resource limits fail.

Quick Commands

Check only file presence and supported extensions:

python scripts/check_complex_inputs.py --protein pocket.pdb --ligand ligand.sdf

Inspect ligand metadata and require a usable 3D conformation for binding graph construction:

python scripts/check_complex_inputs.py \
  --protein pocket.pdb --ligand ligand.sdf --inspect-ligand --remove-hs

Routing Boundaries

Stay in this sub-skill for PDBBind, local protein-ligand complexes, ACNN/PotentialNet binding models, complex graph construction, binding-specific splits, and safe input checks.

Route elsewhere when the task asks for:

• General SMILES/CSV featurizers, MoleculeNet datasets, splitters, or molecular graph validation: use ../molecule-data-prep/SKILL.md.
• General model-zoo architecture catalogs, pretrained GIN checkpoints, molecule embeddings, or non-binding model constructors: use ../model-zoo-pretraining/SKILL.md.
• Generic molecular property prediction loops, metrics, masking, or CSV train/eval workflows: use ../property-prediction/SKILL.md.

Bundled Resources

• references/workflows.md: PDBBind/local complex workflow planning, ACNN/PotentialNet selection, graph construction contracts, and training-smoke guidance.
• references/data-formats.md: supported protein/ligand file formats, coordinates, binding pockets, PDBBind local paths, and configuration flags.
• references/troubleshooting.md: import/install, optional dependency, data/config, CLI/API misuse, conformation, RDKit, layout, and resource-limit fixes.
• scripts/check_complex_inputs.py: offline helper for protein/ligand existence and extension checks, with optional ligand load_molecule metadata inspection.

Safety Notes

The original binding-affinity example runner is long-running and can trigger PDBBind downloads, multiprocessing molecule loading, GPU/CPU training, and multi-epoch evaluation. Treat it as reference-only; adapt the distilled contracts in this sub-skill and run tiny local checks before any full PDBBind execution.