ChemCompass

Retrosynthesis route structures, expert assessments and model feedback in self-contained structured JSON files and flat exports. The structured files embed route context; the flat files contain reaction SMILES and complete assessments, with scoring definitions provided in this repository.

Additional three-class version

The three_class/ version applies to both human cohorts and all four LLMs: original scores 3/4 are removed, and 1 → 1, 2 → 2, 5 → 3. It includes flat and grouped JSON, remapped rubrics with verbatim criteria, and coverage counts. Original scores and rubric IDs remain as provenance fields. Load it with configuration three_class_expert (or another three_class_ subset). The original five-point data and default configuration remain unchanged. The counts below describe the original data; variant counts are in its README.

Dataset counts

Measure	Count
Distinct route structures	171
Reaction occurrences	555
Distinct canonical reactions	552
Expert-annotated route structures	161
Expert-scored step occurrences	519
Distinct reactions in the expert-scored cohorts	517
Individual expert step ratings	991
Consensus step scores	319
Total expert step-score entries	1,310
Original expert whole-route ratings	200
Model feedback annotations	15,945

Human versus LLM step scores

Rubric / assessor	Full distinct reactions	Step-score entries	Flat distinct reactions / rows
Categorical rubric — human	202	991	202 / 991
Categorical rubric — LLMs, four models	202	12,808	202 / 12,808
Development rubric — human consensus	319	319	120 / 120

Four reactions overlap between the human cohorts: their full union is 517 reactions, not 521. The flat human union is 322 reactions with 1,111 score entries. The LLM reaction set is the same 202 reactions as the original human cohort.

Repeated ratings are separate score entries, not separate reactions. The enriched validation table represents the same 200 whole-route rating observations and must not be added to that total again.

Scores grouped by reaction

The default views contain one row per reaction with an assessments list. Grouped expert data has 322 reaction rows and 1,111 assessments. Each object keeps its score, feedback, rubric, evaluator and route reference together. Repeated equal ratings remain separate.

All seven grouped files use the same structure. All seven flat files contain one assessment per row, with the same fields plus reaction_smiles. These are alternative views of the same observations.

Reaction scores, feedback and scope

Every assessment includes score, local_feedback (the existing normalized category), local_feedback_text (the supplied explanation or null), rubric_id, assessor_type, assessor_id, model_id, confidence, and a stable assessment_id. Repository-relative source references identify the original record and reaction occurrence. See the field guide.

These are reaction-linked judgments made within a route: assessment_scope is step_in_route. Neither flattening nor restoring feedback turns them into intrinsic reaction-only feasibility scores.

• Individual humans: 991 ratings; 364 have explanations, 627 are null.
• Four LLMs: 12,808 ratings, all with explanations.
• Included development consensus: 120 scores, no verified step-specific categories or explanations. Route-level reasons remain in the linked full records; they are not copied into step feedback.

A rubric describes what a score means. It does not supply a missing reaction-specific rationale. No new scores or generated explanations were added. The rubric IDs are expert_individual_and_llm and expert_development_consensus, matching sections in rubrics.json.

Schema 3 replaces the grouped scores / rubric_ids arrays with complete assessments objects. To obtain a score list, use [a["score"] for a in row["assessments"].

Scoring rubrics and structured rubric definitions cover the original expert questionnaire, the LLM judge and development consensus criteria verbatim, with source attribution and the complete original LLM prompt. Curation notes are labeled separately. LLM numeric scores are category-derived points, not confidence.

Of the 319 development scores, 120 map safely because every step in each included route has the same score. The remaining 199 have unresolved reaction ordering and remain in the full dataset. The complete expert corpus still has 1,310 score entries; the flat expert file is the safely assignable subset. Uniform scores resolve step ordering only; the development assessment-to-structure links remain inferred from unique canonical targets and matching step counts.

Files

File	Content
all_routes.json	Combined structural catalog
routes.json	Original 50-route catalog
expert_feedback.json	Individual expert reaction and route assessments
expert_consensus_development.json	121 development assessments, 319 step scores and 112 available structures
validation_enriched.json	50-route validation table with 16 anonymized raters, aggregates and model predictions
example_routes.json	Ten example structures and route features
claude_opus48.json	Claude evaluations and response text
gemini_35_pro.json	Gemini evaluations; recorded model ID identifies Gemini 3.1 Pro
gpt55.json	GPT evaluations and response text
llama31_70b.json	Llama evaluations and response text
analysis_report.json	Original-cohort descriptive statistics
cohort_analysis.json	Cohort coverage, overlap and data-quality statistics

See analysis for coverage and label distributions.

Interpretation

• Development assessment 4 and original route 34 share a complete structure.
• Nine development assessments have no available route tree; their route link is null.
• Mixed-score development routes retain recorded step order without an established mapping to reaction nodes. Only order-invariant consensus cases enter the flat export.
• Development mean aggregation floors the mean; validation rounded consensus follows the observed half-up convention. Majority-vote ties remain explicit.
• Missing observations are null. Bibliographic metadata and external URL spans have been removed; structures, scores, labels and analytical distinctions are retained.

Reading a file

After downloading the files, run from this repository's root:

import json
from pathlib import Path

dataset = json.loads(Path("clean/gpt55.json").read_text())
routes = {route["route_id"]: route for route in dataset["routes"]}
evaluation = dataset["records"][0]
route = routes[evaluation["route_id"]]

Read each nested file separately. The table viewer is configured only for the grouped subsets; observations is a loading label, not a train/test split. Verify the release with shasum -a 256 -c SHA256SUMS.

Citation

Zenodo record.