chemaudit-qsar-ready

ChemAudit QSAR-Ready Pipeline

Overview

Configurable 10-step curation pipeline that produces ML-ready canonical SMILES with duplicate detection. Per-step provenance records what changed, enabling audit trails.

Pipeline steps (all toggleable except parse and canonical):

#	Step	Config flag	Default
1	parse — parse input SMILES	always on	on
2	metals — MetalDisconnector	enable_metals	on
3	desalt — LargestFragmentChooser	enable_desalt	on
4	normalize — functional-group Normalizer	enable_normalize	on
5	neutralize — Uncharger	enable_neutralize	on
6	tautomer — TautomerEnumerator canonicalization	enable_tautomer	on
7	stereo — strip stereochemistry	enable_stereo_strip	off
8	isotope — strip isotope labels	enable_isotope_strip	on
9	filter — heavy-atom / MW / inorganic composition filter	min_heavy_atoms, max_heavy_atoms, max_mw, remove_inorganics	3–100 atoms, ≤1500 Da, no inorganics
10	canonical — canonical SMILES + InChIKey	always on	on

QSAR-2D vs QSAR-3D: set enable_stereo_strip: true for 2D descriptors (all stereo removed). Keep it false for 3D workflows that need R/S/E/Z.

InChIKey change detection (D-14)

Every run reports:

• original_inchikey — from parsed input, before any steps.
• standardized_inchikey — from final SMILES, after all steps.
• inchikey_changed — true if the two differ.

inchikey_changed=true is the canonical signal that the pipeline modified the structure in a way that matters for deduplication and downstream ML.

Status values

• ok — successfully curated.
• rejected — failed the composition filter (too few atoms, too heavy, all-inorganic).
• duplicate — InChIKey matches an earlier molecule in the same batch.
• error — pipeline raised an exception (unparseable input, toolkit failure).

Workflow

1. Single molecule

curl -sS -X POST http://localhost:8000/api/v1/qsar-ready/single \
  -H 'Content-Type: application/json' \
  -d '{
    "smiles": "CC(=O)Oc1ccccc1C(=O)O.[Na+].[Cl-]",
    "config": {
      "enable_metals": true,
      "enable_desalt": true,
      "enable_normalize": true,
      "enable_neutralize": true,
      "enable_tautomer": true,
      "enable_stereo_strip": false,
      "enable_isotope_strip": true,
      "min_heavy_atoms": 3,
      "max_heavy_atoms": 100,
      "max_mw": 1500.0,
      "remove_inorganics": true
    }
  }'

Response shape:

{
  "original_smiles": "CC(=O)Oc1ccccc1C(=O)O.[Na+].[Cl-]",
  "original_inchikey": "XXXXXXXX-XXXXXXXXXX-X",
  "curated_smiles": "CC(=O)Oc1ccccc1C(=O)O",
  "standardized_inchikey": "BSYNRYMUTXBXSQ-UHFFFAOYSA-N",
  "inchikey_changed": true,
  "status": "ok",
  "rejection_reason": null,
  "steps": [
    {"step_name": "parse", "step_index": 1, "enabled": true, "status": "applied",
     "before_smiles": null, "after_smiles": "...", "detail": null},
    {"step_name": "metals", "step_index": 2, "enabled": true, "status": "no_change", ...},
    {"step_name": "desalt", "step_index": 3, "enabled": true, "status": "applied",
     "detail": "Removed 2 fragments: Na+, Cl-"},
    ...
  ]
}

Per-step status values: applied, no_change, skipped, error.

2. Batch — file upload

CSV/SDF file:

curl -sS -X POST http://localhost:8000/api/v1/qsar-ready/batch/upload \
  -F 'config={"enable_metals":true,"enable_desalt":true,"enable_normalize":true,"enable_neutralize":true,"enable_tautomer":true,"enable_stereo_strip":false,"enable_isotope_strip":true,"min_heavy_atoms":3,"max_heavy_atoms":100,"max_mw":1500.0,"remove_inorganics":true}' \
  -F "file=@compounds.csv"

config is form-encoded JSON. File is auto-detected (.csv, .sdf, .sd). Phase 9 file pre-validation runs first (M END terminators, encoding, empty rows) — critical issues return HTTP 422 with the pre-validation report.

3. Batch — pasted SMILES (D-03)

When you don't have a file:

curl -sS -X POST http://localhost:8000/api/v1/qsar-ready/batch/upload \
  -F 'config={"enable_metals":true,"enable_desalt":true,...}' \
  -F "smiles_text=CCO
CC(=O)O
c1ccccc1
CC(=O)Oc1ccccc1C(=O)O"

One SMILES per line.

4. Track progress

WebSocket:

const ws = new WebSocket(`ws://localhost:8000/ws/qsar/${job_id}`);
ws.onmessage = (e) => console.log(JSON.parse(e.data));

Or poll:

curl -sS http://localhost:8000/api/v1/qsar-ready/batch/<job_id>/status

5. Paginated results

curl -sS "http://localhost:8000/api/v1/qsar-ready/batch/<job_id>/results?page=1&per_page=50"

Response includes summary with counts: total, ok, rejected, duplicate, error, and steps_applied_counts (per-step count of applied outcomes).

6. Download — CSV / SDF / JSON

curl -sS "http://localhost:8000/api/v1/qsar-ready/batch/<job_id>/download/csv" -o curated.csv
curl -sS "http://localhost:8000/api/v1/qsar-ready/batch/<job_id>/download/sdf" -o curated.sdf
curl -sS "http://localhost:8000/api/v1/qsar-ready/batch/<job_id>/download/json" -o full_provenance.json

CSV columns: original_smiles, curated_smiles, original_inchikey, curated_inchikey, inchikey_changed, status, rejection_reason, steps_applied (comma-joined).

SDF uses curated_smiles as structure, original_inchikey as the molecule title, and attaches original_smiles, curated_smiles, status as properties. Rejected and duplicate entries are skipped.

JSON is the full per-molecule provenance dump plus summary, config, and a duplicates list.

Examples

Example 1 — "Curate this SMILES and show what changed"

1. POST /api/v1/qsar-ready/single with config=default.
2. Print original_smiles, curated_smiles, inchikey_changed.
3. Walk steps[]; for each step where status == "applied", print step_name → detail.

Example 2 — "QSAR-2D curation on this SDF, download cleaned CSV"

1. POST /api/v1/qsar-ready/batch/upload with config.enable_stereo_strip=true and the file.
2. WS or poll until status=complete.
3. GET /api/v1/qsar-ready/batch/<job_id>/results → check summary (counts per status).
4. GET /api/v1/qsar-ready/batch/<job_id>/download/csv → 2D-stripped, deduplicated SMILES.

Example 3 — "How many molecules changed InChIKey after standardization?"

From the results summary isn't enough — you need per-molecule detail:

1. GET /api/v1/qsar-ready/batch/<job_id>/results?page=1&per_page=500 (or iterate pages).
2. Count [r for r in results if r["inchikey_changed"]].
3. Slice: [r["original_smiles"] for r in results if r["inchikey_changed"] and r["status"] == "ok"].

Rate limits

• /qsar-ready/single: 30/min.
• /qsar-ready/batch/upload: 3/min.
• /qsar-ready/batch/<job_id>/status: 60/min.
• /qsar-ready/batch/<job_id>/results: 30/min.
• /qsar-ready/batch/<job_id>/download/<format>: 10/min.

Troubleshooting

400 "Invalid config JSON"

config must be a JSON-encoded string in the multipart form. Quote it once, don't double-encode.

400 "Either a file or smiles_text must be provided"

Batch upload needs one of the two. Pasted SMILES goes under the smiles_text form field, not text or smiles.

422 "File has critical pre-validation issues"

Response body has a prevalidation field with the FilePreValidationResponse payload. Most common causes: missing M END in SDF, encoding mismatch in CSV, empty file. Fix the file and re-upload.

400 "No valid SMILES found in input"

File parsed OK but every row had a parse_error. Ensure the SMILES column exists (CSV auto-detects only that exact name) or pre-validate with /diagnostics/file-prevalidate.

400 "Input contains N molecules. Maximum allowed is M."

Hit the deployment-profile batch cap. GET /api/v1/config to read the limit, split the file, or redeploy with a larger profile.

status = "rejected" with rejection_reason = "Molecule has no carbon atoms"

The composition filter (step 9) rejected an inorganic. Set remove_inorganics: false in config to keep them.

status = "duplicate"

Another earlier molecule in the same batch canonicalized to the same InChIKey. Intentional — keeps only the first occurrence. Duplicates are written to the JSON download under duplicates[].

Tautomer canonicalization stripped E/Z double-bond stereo

Known behavior of TautomerEnumerator. If that matters, set enable_tautomer: false — but expect that molecules with different tautomeric forms of the same compound will produce distinct InChIKeys.

Further reading

• chemaudit-standardization — single-molecule standardization with richer provenance.
• chemaudit-dataset-intelligence — health audit across a dataset, complementary to curation.
• chemaudit-diagnostics — pre-flight file validation.