scoring

Scoring

Use this sub-skill when a task mentions scoring.toml, run_type = "scoring", scoring components, transforms, aggregation, comp_* plugin files, @add_tag, QED, MolecularWeight, TanimotoSimilarity, TanimotoDistance, OpenEye ROCS, Chemprop, ExternalProcess, or REST scoring.

Scope

This skill covers scoring-only jobs and scoring-function design that will later be reused by staged learning. For model sampling setup, use the sibling sampling skill. For transfer-learning or staged-learning orchestration, use the learning skill and return here only for [scoring] or [stage.scoring] blocks. For SMILES preprocessing, use the data-pipeline skill.

Start Here

1. Use run_type = "scoring" for a standalone scoring run and provide a [parameters] block with smiles_file and optional output_csv.
2. Build a [scoring] block with type = "geometric_mean" or type = "arithmetic_mean", optional parallel, optional use_pumas, and one or more [[scoring.component]] blocks.
3. Keep each component block in this shape: [[scoring.component]], [scoring.component.ComponentName], then one or more [[scoring.component.ComponentName.endpoint]] tables with name, optional weight, optional params.*, and optional transform.*.
4. Prefer a scoring-only run on representative SMILES before using the same scoring function in RL.

References

• references/components-and-plugins.md: component block shapes, common built-ins, external components, and custom plugin discovery.
• references/transforms-and-aggregation.md: transform choice, endpoint weights, geometric/arithmetic aggregation, filters, and penalties.
• references/troubleshooting.md: config parsing, plugin import, optional dependency, external service, and transform-debugging failures.

Bundled Helpers

• scripts/validate_scoring_config.py: parse a TOML scoring config and summarize scoring blocks, components, endpoints, params, transforms, and likely structural issues without running scoring.
• scripts/list_scoring_components.py: inspect installed reinvent_plugins.components namespace packages and list discovered comp_* modules/classes without executing a scoring job.

Run each helper with --help first. Both helpers are read-only; they inspect installed Python packages and config files but do not launch reinvent, score molecules, call external services, or load model checkpoints intentionally.

Practical Defaults

• Use QED without a transform because it already returns a 0-1 value.
• Use MolecularWeight with double_sigmoid for a preferred mass window such as 200-500 Da.
• Use TanimotoSimilarity for new configs; treat TanimotoDistance as a backward-compatible deprecated alias.
• Use custom_alerts for must-fail SMARTS filters and do not rely on its weight for optimization.
• Use geometric_mean when every objective must remain acceptable; use arithmetic_mean when compensation between objectives is acceptable.