chemaudit-dataset-intelligence

name	chemaudit-dataset-intelligence
description	Audit dataset health with a Fourches-style 5-component score, detect contradictory bioactivity labels across duplicates, compare two dataset versions (added / removed / modified / unchanged), and generate curation reports. Use when user says "audit this dataset", "dataset health score", "contradictory labels", "duplicate activity conflict", "dataset diff", "compare dataset versions", "curation report", "data quality check", or "clean this dataset for ML". Accepts CSV and SDF with optional activity columns.
license	MIT
allowed-tools	Bash(curl:*) Bash(python:*)
compatibility	Requires a running ChemAudit instance with Celery workers for async audits.
metadata	{"author":"Kohulan Rajan","version":"1.0.0","mcp-server":"chemaudit","category":"cheminformatics","tags":["dataset-curation","quality-control","ML-datasets","bioactivity","data-cleaning"]}

ChemAudit Dataset Intelligence

Overview

Whole-dataset auditing — operates on the file, not individual molecules. Four outputs:

1. Health audit — composite 0-100 score with 5-component breakdown (Fourches 2010).
2. Contradictory labels — duplicate InChIKeys with inconsistent activity values.
3. Dataset diff — structural and property-level comparison between two uploads.
4. Curation report + curated CSV — JSON report and downloadable cleaned CSV with appended diagnostic columns.

Health audit — 5 sub-scores (Fourches 2010)

Sub-score	Default weight	What it measures
parsability	0.25	Fraction of rows with a valid, parseable SMILES
stereo	0.15	Fraction with defined stereocenters where chirality is expected
uniqueness	0.20	1 - (duplicates / total) by canonical InChIKey
alerts	0.20	Fraction of molecules alert-free against the default catalogs
std_consistency	0.20	Fraction agreeing across three standardization pipelines (RDKit vs ChEMBL vs minimal) on a sampled subset

Weights are customizable per audit; source of literature-backed defaults is DEFAULT_WEIGHTS in frontend/src/types/dataset_intelligence.ts.

Overall score is the weighted sum × 100.

Contradictory labels — fold-difference thresholds

For every InChIKey appearing ≥ 2 times with a numeric activity column, reports the max/min fold-difference. Threshold options: 3, 5, 10, 50, 100 (default: 10). Entries above the threshold are flagged as contradictory.

Workflow

1. Upload dataset

curl -sS -X POST http://localhost:8000/api/v1/dataset/upload \
  -F "file=@bioactivity.csv" \
  -F "smiles_column=canonical_smiles" \
  -F "activity_column=pIC50"

Both column names are optional — auto-detection runs if omitted. Accepts .csv and .sdf. Response:

{
  "job_id": "3f7a9b...",
  "filename": "bioactivity.csv",
  "file_type": "csv",
  "status": "pending",
  "message": "Dataset audit job submitted"
}

2. Track progress

WebSocket:

const ws = new WebSocket(`ws://localhost:8000/ws/dataset/${job_id}`);

Poll:

curl -sS http://localhost:8000/api/v1/dataset/<job_id>/status

Response: {job_id, status, progress, current_stage, eta_seconds}. Stages: parse → health_audit → contradictions → standardization_sample → diff_ready.

3. Fetch audit results

curl -sS http://localhost:8000/api/v1/dataset/<job_id>/results

While processing, returns HTTP 202 with {job_id, status, message}. When complete, returns:

{
  "job_id": "...",
  "status": "complete",
  "health_audit": {
    "overall_score": 78.3,
    "sub_scores": [
      {"name": "parsability", "score": 0.985, "weight": 0.25, "count": 15, "total": 1000},
      {"name": "stereo", "score": 0.62, "weight": 0.15, "count": 380, "total": 1000},
      {"name": "uniqueness", "score": 0.93, "weight": 0.20, "count": 70, "total": 1000},
      {"name": "alerts", "score": 0.81, "weight": 0.20, "count": 190, "total": 1000},
      {"name": "std_consistency", "score": 0.88, "weight": 0.20, "count": 12, "total": 100}
    ],
    "weights": {"parsability": 0.25, "stereo": 0.15, "uniqueness": 0.20, "alerts": 0.20, "std_consistency": 0.20},
    "molecule_count": 1000,
    "issues": [
      {"row_index": 42, "smiles": "C(C)(C)(C)(C)C", "issue_type": "parse_error", "severity": "error", "description": "..."}
    ],
    "property_distributions": {
      "mw": {"bins": [...], "counts": [...]},
      "logp": {"bins": [...], "counts": [...]},
      "tpsa": {"bins": [...], "counts": [...]}
    },
    "std_pipeline_comparison": {...},
    "std_sample_size": 100,
    "dedup_groups": [{"inchikey": "...", "rows": [12, 88]}]
  },
  "contradictions": [
    {
      "inchikey": "BSYNRYMUTXBXSQ-UHFFFAOYSA-N",
      "entries": [
        {"row_index": 5, "smiles": "...", "activity": 7.2},
        {"row_index": 203, "smiles": "...", "activity": 4.1}
      ],
      "fold_difference": 1259.0,
      "entry_count": 2,
      "smiles": "CC(=O)Oc1ccccc1C(=O)O"
    }
  ],
  "numeric_columns": [
    {"name": "pIC50", "priority": 1},
    {"name": "MW", "priority": 2}
  ],
  "curation_report": {...},
  "curated_csv_available": true
}

4. Compare two dataset versions (diff)

Upload the comparison file against a completed primary job:

curl -sS -X POST http://localhost:8000/api/v1/dataset/<job_id>/diff \
  -F "file=@bioactivity_v2.csv"

Response:

{
  "added": [{"inchikey": "...", "smiles": "...", "row_index": 12, "properties": {...}, "changes": []}],
  "removed": [...],
  "modified": [{"inchikey": "...", "changes": [{"column": "pIC50", "old_value": 6.2, "new_value": 7.1}]}],
  "added_count": 45,
  "removed_count": 12,
  "modified_count": 89,
  "unchanged_count": 854,
  "unique_columns_primary": 2,
  "unique_columns_comparison": 3
}

Diff uses InChIKey as the primary key, so it's robust to SMILES notation differences.

5. Downloads

# Full curation report JSON
curl -sS http://localhost:8000/api/v1/dataset/<job_id>/download/report \
  -o curation_report.json

# Curated CSV — original rows + appended diagnostic columns
curl -sS http://localhost:8000/api/v1/dataset/<job_id>/download/csv \
  -o curated.csv

Curated CSV appends: _health_issues (joined issue types), _is_duplicate (bool), _standardized_smiles, _alert_flags (joined catalog names).

Examples

Example 1 — "Audit this ChEMBL bioactivity CSV for quality issues"

1. POST /dataset/upload with smiles_column and activity_column pointing at your CSV.
2. Poll /status or WS until complete.
3. GET /results → read health_audit.overall_score (aim ≥ 70) and health_audit.sub_scores[] for the weakest component.
4. Read contradictions[] (keeps only groups above the default 10-fold threshold — check entry_count and fold_difference per group).
5. If any issues have severity="error", those rows need manual review.

Example 2 — "Compare v1 and v2 of a training set"

1. Upload v1 and wait for complete → capture job_id_v1.
2. POST /dataset/<job_id_v1>/diff with the v2 file.
3. Read added_count, removed_count, modified_count for an overview.
4. For details on changed labels: modified[] contains changes[] arrays — look for activity-column changes.

Example 3 — "Generate a curation report for my supervisor"

1. Audit as in example 1.
2. GET /download/report → JSON you can paste into an appendix or render as markdown.
3. GET /download/csv → drop-in replacement dataset with diagnostic columns.

Rate limits

• /dataset/upload: 3/min.
• /dataset/<job_id>/status: 60/min.
• /dataset/<job_id>/results: 30/min.
• /dataset/<job_id>/diff: 10/min.
• /dataset/<job_id>/download/*: 10/min.

Troubleshooting

400 "Invalid file type. Please upload a CSV or SDF file."

Only .csv and .sdf extensions accepted — .tsv, .txt, etc. are rejected by dataset-intelligence (unlike batch validation which accepts all four).

413 "File exceeds maximum size limit"

Check /api/v1/config for the deployment limit. Split the file or redeploy with a larger profile.

202 response on /results

Job is still running. Keep polling /status (lighter endpoint) until status flips to complete or error.

400 "Primary dataset job is not yet complete"

Diff endpoint needs the primary job to finish first. Wait until the primary shows status=complete in /status.

activity_column auto-detected wrong column

Specify it explicitly. The auto-detector ranks by name heuristics and numeric-column detection; numeric columns are returned in numeric_columns[] sorted by priority.

contradictions empty when you expect conflicts

The default fold threshold is 10. Lower-fold conflicts aren't reported unless you customize the threshold (currently set server-side; contact admin to change). Check entries[] length — duplicates below threshold are listed but won't appear as contradictions.

std_consistency score very low

All three standardization pipelines (RDKit vs ChEMBL vs minimal) disagreed for many molecules. Usually means the dataset has unusual edge cases (metal complexes, large fragments, unusual charges). Look at std_pipeline_comparison for per-pipeline statistics.

No curated_csv_available in results

The Celery worker failed mid-curation-report step. Re-upload. If it persists, check worker logs.

Further reading

• chemaudit-qsar-ready — once the audit reveals issues, curate per molecule with provenance.
• chemaudit-standardization — per-molecule investigation of why two SMILES share an InChIKey.
• chemaudit-diagnostics — pre-flight file integrity checks before upload.