onepot-python

Python client for the onepot API — find purchasable analogs of your query molecules, price exact molecules directly, with optional retrosynthesis decomposition and per-position building-block filtering.

Installation

uv add onepot
# or
pip install onepot

Quick start

from onepot import Client

client = Client(api_key="your-api-key")

resp = client.search(
    smiles_list=["c1ccc(NC(=O)c2ccccc2)cc1"],
    max_results=10,
)
for r in resp["queries"][0]["results"]:
    print(r["smiles"], r["similarity"], r["price_usd"])

Features

• Similarity search — Tanimoto nearest analogs from the onepot catalog
• Exact pricing — price the exact query molecule directly, with an opt-in stereo-relaxed fallback; fast, cheap bulk pricing of pre-enumerated libraries
• Substructure search — purchasable molecules containing a SMILES/SMARTS pattern
• Decomposition + BB filters — inspect the retro paths the system considered for your query, then refine which candidate BBs are eligible per position
• Risk and price filters — exclude results above a chemistry-risk, supplier-risk, or price threshold
• Streaming — single-molecule searches with SSE progress updates
• Ordering — submit results for synthesis quoting

Search

Basic

resp = client.search(smiles_list=["c1ccc(-c2ccccc2)cc1"], max_results=10)

curl -X POST https://api.onepot.ai/v1/search \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-api-key" \
  -d '{"smiles_list": ["c1ccc(-c2ccccc2)cc1"], "max_results": 10}'

Exact lookup

Use exact_lookup=True to price each query molecule directly and skip the analog/similarity search. Each query returns at most one result — the query molecule itself (similarity 1.0), priced from a catalog match or its cheapest single-step decomposition — or no result if it can't be made from catalog building blocks. It never substitutes an analog. This is the fast, cheap path for bulk pricing of a pre-enumerated library, and bills at 0.1× (see Pricing).

resp = client.search(
    smiles_list=my_enumerated_library,   # e.g. thousands of SMILES
    exact_lookup=True,
    include_chemistry_risk=True,
)
for q in resp["queries"]:
    if q["results"]:
        print(q["query_smiles"], q["results"][0]["price_usd"])
    else:
        print(q["query_smiles"], "not priceable")

curl -X POST https://api.onepot.ai/v1/search \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-api-key" \
  -d '{"smiles_list": ["c1ccc(NC(=O)c2ccccc2)cc1"], "exact_lookup": true}'

The response uses the standard shape, with results holding 0 or 1 entry per query. Exact-lookup results are not annotated with reaction_class / bbs. Cannot be combined with substructure_search, decompose, or bb_filters (rejected as 422). Streaming supports it too via client.search_stream(..., exact_lookup=True).

To accept a racemate or unspecified-stereo structure when strict identity cannot be priced, pass no_stereo=True. Strict identity is always attempted first. A fallback result contains the stereochemistry-stripped smiles / inchikey and "stereo_relaxed": true.

resp = client.search(
    smiles_list=["N[C@@H](C)C(=O)O"],
    exact_lookup=True,
    no_stereo=True,
)

no_stereo=True also applies to the guaranteed exact candidate included in an ordinary similarity search. It cannot be combined with substructure_search or bb_filters.

Streaming

For single-molecule searches with real-time progress events. Status lifecycle: starting → synthesis → rescoring → complete. The final event includes a results list with the same fields as the batch endpoint.

for event in client.search_stream("c1ccc(NC(=O)c2ccccc2)cc1", max_results=10):
    print(event["status"], event["message"])
    if event["status"] == "complete":
        results = event["results"]

curl -sN -X POST https://api.onepot.ai/v1/search/stream \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-api-key" \
  -d '{"smiles": "c1ccc(NC(=O)c2ccccc2)cc1", "max_results": 5}'

Substructure search

Pass substructure_search=True to return purchasable molecules that contain the query as a substructure, instead of similarity hits. The query can be a SMILES or a SMARTS pattern.

resp = client.search(
    smiles_list=["c1ccc(C(=O)N)cc1"],
    max_results=10,
    substructure_search=True,
)

curl -X POST https://api.onepot.ai/v1/search \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-api-key" \
  -d '{"smiles_list": ["c1ccc(C(=O)N)cc1"], "max_results": 10, "substructure_search": true}'

Risk and price filters

All optional. When set, results that exceed the threshold are excluded.

Setting max_chemistry_risk automatically includes the chemistry_risk field in the response. Pass include_chemistry_risk_score=True for the raw probability score.

resp = client.search(
    smiles_list=["c1ccc(-c2ccccc2)cc1"],
    max_results=10,
    max_price=500,
    max_supplier_risk="medium",
    max_chemistry_risk="low",
    include_chemistry_risk_score=True,
)

Decompose & bb_filters

Use decompose=True to receive the retrosynthetic paths the system considered for each query — every reaction_class it found and the BB SMILES of your query at each position. Then call back with bb_filters to constrain which candidate BBs are eligible per position. Every enumerated result is automatically tagged with the reaction_class it was made from and the bbs that built it.

Call 1 — discover.

resp = client.search(
    smiles_list=["c1ccc(NC(=O)c2ccccc2)cc1"],
    max_results=5,
    decompose=True,
)
decompositions = resp["queries"][0]["decompositions"]
rxn = decompositions[0]["reaction_class"]   # e.g. "rxn_5e820be4"

Call 2 — refine. Force the building block at position 1 to vary (Tanimoto ≤ 0.7 to the query's position-1 BB) while leaving position 0 free.

resp = client.search(
    smiles_list=["c1ccc(NC(=O)c2ccccc2)cc1"],
    max_results=10,
    bb_filters=[{"reaction_class": rxn, "bb_index": 1, "max_similarity": 0.7}],
)
for r in resp["queries"][0]["results"]:
    if r.get("reaction_class") == rxn:
        bb_smiles = [b["smiles"] for b in r["bbs"]]
        print(r["smiles"], "←", " + ".join(bb_smiles))

curl -X POST https://api.onepot.ai/v1/search \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-api-key" \
  -d '{
    "smiles_list": ["c1ccc(NC(=O)c2ccccc2)cc1"],
    "max_results": 10,
    "bb_filters": [
      {"reaction_class": "rxn_<from-call-1>", "bb_index": 1, "max_similarity": 0.7}
    ]
  }'

reaction_class values like "rxn_5e820be4" come from a prior decompose=True response and are stable across calls — pass them through as strings. Each bb_filters entry takes optional min_similarity and max_similarity (Tanimoto, 0.0–1.0); omit a bound to leave that side open. Combine multiple entries to constrain multiple positions in one call. bb_index is the 0-based position of the building block within the reaction, matching the ordering in the bbs field of a decomposition or annotated result. Unknown reaction_class or min_similarity > max_similarity is rejected as 422. Streaming searches accept the same parameters via client.search_stream(...).

When a retro decomposition produces multiple paths under the same reaction_class, filters apply to each path's candidates independently (similarity is measured against that path's BB SMILES, so the same SMILES can pass one path's filter and fail another's).

Response shape

{
    "queries": [
        {
            "query_smiles": "c1ccc(NC(=O)c2ccccc2)cc1",
            "query_inchikey": "...",
            "results": [
                {
                    "smiles": "...",
                    "inchikey": "...",
                    "similarity": 0.92,
                    "price_usd": 590,
                    "supplier_risk": "low",
                    "chemistry_risk": "medium",       # if include_chemistry_risk=True
                    "chemistry_risk_score": 0.5,      # if include_chemistry_risk_score=True
                    # present on enumerated results (synthesized analogs):
                    "reaction_class": "rxn_5e820be4",
                    "bbs": [
                        {"bb_index": 0, "smiles": "<bb-smiles>"},
                        {"bb_index": 1, "smiles": "<bb-smiles>"},
                    ],
                },
                ...
            ],
            # if decompose=True:
            "decompositions": [
                {
                    "reaction_class": "rxn_5e820be4",
                    "bbs": [
                        {"bb_index": 0, "smiles": "<bb-smiles>"},
                        {"bb_index": 1, "smiles": "<bb-smiles>"},
                    ],
                },
                ...
            ],
        },
        ...
    ],
    "credits_used": 10,
    "credits_remaining": 990,
}

Order

Submit results for synthesis quoting. Returns an order_id you can reference in followup.

order = client.order(
    smiles=["CCO", "c1ccccc1"],
    email="you@example.com",
    notes="Optional notes",
)
# {"order_id": "a1b2c3d4-...", "molecule_count": 2}

curl -X POST https://api.onepot.ai/v1/order \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-api-key" \
  -d '{
    "smiles": ["CCO", "c1ccccc1"],
    "email": "you@example.com",
    "notes": "Optional notes"
  }'

Pricing

Credits are charged per SMILES in the query, by mode and chemistry-risk tier:

decompose, bb_filters, and substructure_search don't change the price.

exact_lookup=True bills at 0.1× the full-search rate (it skips the analog search). The total is charged as a whole number per request — the per-SMILES rate × molecule count, rounded, with a minimum of 1 credit per request. So a 5,000-molecule exact base search costs 500 credits, while a single molecule costs 1.