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name: reaction-prediction description: "Plan and troubleshoot DGL-LifeSci WLN reaction center prediction and candidate ranking workflows with USPTO datasets, reaction SMILES, candidate bond files, and rexgen-direct style commands." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Reaction Prediction

Use this sub-skill when a task involves DGL-LifeSci reaction prediction with Weisfeiler-Lehman Networks (WLN):

• Prepare mapped reaction SMILES inputs for reaction center prediction.
• Choose between WLNCenterDataset, USPTOCenter, WLNRankDataset, and USPTORank.
• Instantiate or reason about WLNReactionCenter and WLNReactionRanking tensors and constructor dimensions.
• Plan the rexgen-direct two-stage flow: center prediction first, candidate bond generation second, candidate product ranking last.
• Diagnose invalid reaction files, candidate-bond mismatches, reaction-size filtering, and long training/download boundaries.

Start Here

1. Read references/workflows.md to decide whether the task is center prediction, candidate ranking, or the full two-stage pipeline.
2. Read references/data-formats.md before creating or validating reaction files, processed graph-edit files, or candidate-bond files.
3. Run scripts/validate_reaction_inputs.py --help, then validate tiny custom reaction fixtures before constructing WLN datasets.
4. Use references/troubleshooting.md when parsing, dataset construction, pretrained downloads, multiprocessing, or config compatibility fails.

Quick Validation

Validate a custom reaction text file without training, downloads, DGL, or RDKit:

python scripts/validate_reaction_inputs.py --reactions train.txt --max-rows 100 --max-length 5000 --require-atom-maps

Validate candidate ranking alignment after candidate bonds have been produced:

python scripts/validate_reaction_inputs.py \
  --reactions train_valid_reactions.proc \
  --candidate-bonds train_candidate_bonds.txt \
  --processed --max-rows 200

Routing Boundaries

Stay in this sub-skill for WLN reaction center prediction, WLN candidate ranking, USPTO reaction datasets, reaction SMILES with atom mapping, candidate-bond files, and rexgen-direct command planning.

Route elsewhere when the task asks for:

• Generic SMILES parsing, molecule featurizers, graph construction, or CSV datasets: use ../molecule-data-prep/SKILL.md.
• General model constructor catalog, pretrained model naming patterns, or non-reaction model selection: use ../model-zoo-pretraining/SKILL.md.
• Molecular property prediction train/eval workflows: use ../property-prediction/SKILL.md.

Bundled Resources

• references/workflows.md: two-stage WLN workflow, dataset/model APIs, config fields, command classes, and safety notes.
• references/data-formats.md: reaction SMILES, processed graph edits, candidate-bond rows, cutoffs, and validation expectations.
• references/troubleshooting.md: import/install, optional dependency, reaction parsing, candidate generation, config, multiprocessing, and long-run failure modes.
• scripts/validate_reaction_inputs.py: safe offline validator for reaction text files and candidate-bond row alignment.