{"claim":{"id":"e65fab13-24a4-44e2-a1e5-a489f7c23fd0","submission_id":"d6ca2639-8b09-4f51-b7cb-033f2065179e","submission_revision_id":"e541f1d7-eb9b-446c-bc4a-3dd250f3b2d1","builder":"builder-01","status":"active","claimed_at":"2026-10-15T15:38:14.520734Z","delivered_at":null,"ended_at":null,"delivery_note":null,"end_reason":null},"task":{"submission_id":"d6ca2639-8b09-4f51-b7cb-033f2065179e","submission_revision_id":"e541f1d7-eb9b-446c-bc4a-3dd250f3b2d1","revision_version":2,"submission_status":"under_review","submitted_at":"2026-10-06T02:27:44.721392Z","submitter_name":"Ayleen Farnood","submitter_email":"ayleenf@mit.edu","task":{"status":"submitted","version":2,"extra_fields":{"difficulty_self_test":{"model":"Claude Opus 5.5","harness":"Claude Code","estimated_score":"20–40%"}},"submitted_at":"2026-10-06T02:27:44.721392+00:00","industry_domain":"Chemistry & Materials Computation","task_short_name":"ligand_catoms_instance1","operating_system":"Linux","task_description":"Objective: Determine how each of 100 ligands binds a metal by identifying which atoms coordinate to the metal centre and determining its denticity. \n\nStarting State:\nligands.csv: 100 ligands (ligand_id, smiles, atoms). Each ligand was observed bound to a single metal in at least two experimental crystal structures of mononuclear transition-metal complexes. 'smiles' is the free ligand as written by RDKit, with formal charges where the ligand is anionic. 'atoms' lists every heavy atom as index:element in RDKit order, so atoms can be referred to by index.\noutput/predictions_template.csv: one row per ligand to fill in.\nTASK.md: these instructions in full.\n\nInstructions & Rules:\nFor every ligand, decide which atoms bind the metal. For π-bound (haptic) ligands, list every metal-bound atom.\nSave output/predictions.csv with columns ligand_id, coordinating_atoms, denticity. coordinating_atoms is a space-separated list of atom indices; denticity is an integer.\nWork offline. Do not use crystal-structure or ligand databases or trained ligand-coordination models. RDKit, numpy, pandas and networkx may be used.","software_licensing":"Free/Open Source","evaluation_criteria":null,"verification_method":"Run python3 score.py <agent_dir>. It reads output/predictions.csv and compares it with the reference.\n\nScoring rule: for each of the 100 ligands, the prediction is correct only if its set of coordinating-atom indices equals the observed set, or a set equivalent to it by the ligand's symmetry or resonance (for example, either oxygen of a carboxylate, or either of two symmetry-equivalent pyridine nitrogens). Order doesn't matter; no partial credit for overlap. All accepted sets are listed in reference_answers.csv.\nScore: the fraction of the 100 ligands that are correct. Denticity accuracy is reported as a secondary metric and is judged independently of the atom set.\nInvalid rows: missing, duplicate or unparsable rows score as wrong.\nControls: the reference file scores 1.0 and the empty template scores 0.0.\n\nHow the reference was made: the ligands come from the held-out test and holdout sets of pydentate (Toney et al., PNAS 2025). They were restricted to ligands whose binding mode is seen in at least two crystal structures. All 100 are ligands for which the pydentate GNN predicts the coordinating atoms incorrectly, also when symmetry- and resonance-equivalent answers are accepted. In the source data every ligand has exactly one observed binding mode, so each answer is unique up to the ligand's symmetry and resonance.","software_and_version":"Python 3.11 with RDKit 2026.03, numpy, pandas, networkx","specific_requirements":null,"expected_completion_time":null},"files":[{"file_id":"01806bf1-eb35-4677-a525-172acac07e33","filename":"input_materials.zip","category":"input_materials","content_type":"application/zip","size_bytes":6409},{"file_id":"cf2683af-3a7f-4de0-97c6-8185b9c59a99","filename":"reference_output.zip","category":"reference_output","content_type":"application/zip","size_bytes":10655},{"file_id":"828ee5a2-3a91-4588-ba21-b21c2b16c7a4","filename":"score.txt","category":"difficulty_self_test_evidence","content_type":"text/plain","size_bytes":95},{"file_id":"d27997ae-8b7f-4f85-8474-2d2324ad7708","filename":"predictions.csv","category":"difficulty_self_test_evidence","content_type":"text/csv","size_bytes":1584}],"deep_review":{"overall_verdict":"Accept","review_text":"**Overall Verdict:** Accept — The revision is complete and internally consistent, with reproducible scoring evidence and a viable offline workflow.\n\n**Completeness Check:**\n\n- Instruction: Complete — Defines the prediction objective, atom indexing, haptic binding convention, restrictions, and required output schema.\n- Inputs: Complete — All 100 ligand records align with the template and references; supplied atom maps match the SMILES atom-token sequences.\n- Toolchain: Complete — The open-source Python stack supports offline completion on one ordinary machine; scoring requires only the standard library.\n- Reference output: Complete — Includes 100 observed answers and 142 accepted sets, with valid index ranges and consistent denticities.\n- Rubric: Complete — Exact-set accuracy, independent denticity accuracy, and invalid-row handling are defined. Independent comparisons confirm both controls and the reported 39% atom-set and 51% denticity self-test scores.\n\n**File and Task Reachability:**\n\nAttachments: `reachable`. Task: `reachable`.\n\nInspected all four attachments: `input_materials.zip`, `reference_output.zip`, `score.txt`, and `predictions.csv`. Archive inspection covered `ENVIRONMENT.md`, `ATTRIBUTION.md`, `TASK.md`, `ligands.csv`, `predictions_template.csv`, `GRADING.md`, `predictions_reference.csv`, `reference_answers.csv`, `score.py`, and `build_equivalent_sets.py`. File types and byte counts match the manifest; both archives are readable, unencrypted, and contain no unsafe paths or special entries. Text and CSV content is valid UTF-8, and no attachment content remains unread.\n\nThe completion path is to provision dependencies before network isolation, predict from the solver-visible inputs, produce the required CSV, and evaluate against separately held references. No answer leakage was found in the input archive. Uploaded code was inspected without execution. RDKit molecular validation and equivalence enumeration, external provenance, and the claimed self-test model identity were not independently verified.\n\nLicense keys, accounts, tokens, and other credentials are not required from the contributor. Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.\n\n**Special Screening:**\n\n`public_benchmark` — The submission explicitly reuses pydentate test/holdout examples and observed labels from an identified public data release. Strict offline execution mitigates answer retrieval. External verification was unavailable, so current answer retrievability and pretraining exposure remain uncertain.\n\n**Recommended Changes:**\n\nThe submission provides consistent data, separated grading assets, and corroborated scoring evidence. No contributor changes are required. Preserve strict network isolation and evaluator-only access to references and self-test predictions.","reviewed_at":"2026-10-06T02:32:51.056290Z"},"claim":{"id":"e65fab13-24a4-44e2-a1e5-a489f7c23fd0","submission_id":"d6ca2639-8b09-4f51-b7cb-033f2065179e","submission_revision_id":"e541f1d7-eb9b-446c-bc4a-3dd250f3b2d1","builder":"builder-01","status":"active","claimed_at":"2026-10-15T15:38:14.520734Z","delivered_at":null,"ended_at":null,"delivery_note":null,"end_reason":null}},"revision_is_current":true}