Zinc Database

ZINC Chemical Library Database (ZINC22 / CartBlanche)

Overview

ZINC (ZINC Is Not Commercial) is a free database of commercially available compounds curated for virtual screening. The current generation, ZINC22, holds billions of make-on-demand and in-stock molecules and is served through the CartBlanche web application at cartblanche22.docking.org. CartBlanche exposes a small JSON API to look up substances by ZINC ID, resolve a SMILES to its ZINC identifier, and inspect supplier/catalog purchasability.

Important — the old ZINC15 REST API no longer works for automated access. zinc15.docking.org (and zinc.docking.org) now sits behind a site-wide CAPTCHA: every request to /substances.json, /tranches/..., /substances/{id}.json, etc. is redirected to a /captcha/ page or returns 403 Forbidden. Any script using the mwt__gte / availability / similarity query parameters against zinc15.docking.org will fail. Use the ZINC22 CartBlanche endpoints documented below instead.

Three things about ZINC22/CartBlanche that change how you use this skill:

1. There is no server-side property-range query. ZINC22/CartBlanche has no mwt__gte / logp__lte / hbd__lte search. You filter by molecular property by either (a) selecting tranches (a 2D MW x logP grid) for bulk download, or (b) retrieving compounds and filtering locally with RDKit. This skill shows the local-RDKit approach.
2. SMILES lookup is asynchronous. You POST a SMILES, receive a task id, and poll for the result. Exact-match lookup completes in seconds.
3. Reliable programmatic search = exact match. The API's broader analog search (dist > 0) is slow and frequently times out on the public server, and the Smallworld similarity search is a website-only flow. Treat exact SMILES → ZINC ID lookup as the dependable primitive; see the "Analog / Similarity Search" note for the (limited) alternatives.

When to Use

• Looking up a known ZINC ID to get its SMILES, computed properties, and purchasability/suppliers
• Resolving a molecule you have as a SMILES to its ZINC22 identifier(s) to check availability
• Checking whether a hit compound is purchasable and from which catalogs before ordering
• Assembling a SMILES/ZINC-ID set to feed into a docking campaign
• For property-filtered library building, combine SMILES/tranche retrieval here with local RDKit filtering (rdkit-cheminformatics)
• For known drug bioactivity data use chembl-database-bioactivity; for approved drug structures use drugbank-database-access

Prerequisites

• Python packages: requests, pandas (and rdkit for local property filtering)
• Data requirements: a ZINC ID, or a SMILES string
• Environment: internet connection; no API key needed; SMILES lookups are async (poll for the result)
• Rate limits: be courteous — serialize searches, cache results, and do not poll the task endpoint faster than once every few seconds

pip install requests pandas
# optional, for local property filtering:
pip install rdkit

Quick Start

import requests

BASE = "https://cartblanche22.docking.org"
HEADERS = {"User-Agent": "sciagent-zinc-skill/1.0"}

# Look up a substance by ZINC ID (synchronous, returns JSON immediately)
r = requests.get(f"{BASE}/substance/ZINC000000029632.json", headers=HEADERS, timeout=30)
r.raise_for_status()
c = r.json()

td = c["tranche_details"]
print(f"ZINC ID : {c['zinc_id']}  (db: {c['db']})")
print(f"SMILES  : {c['smiles']}")
print(f"MW      : {td['mwt']:.2f}   logP: {td['logp']:.2f}   heavy atoms: {td['heavy_atoms']}")
print(f"InChIKey: {td['inchikey']}")
print(f"Catalogs: {len(c.get('catalogs', []))} supplier entries")

Expected output (abridged):

ZINC ID : ZINC000000029632  (db: zinc20)
SMILES  : O=C([C@H]1CCCN1C(=O)Cc1c[nH]c2ccccc12)N1CCc2ccccc2C1
MW      : 387.48   logP: 3.29   heavy atoms: 29
InChIKey: QCEMBLKSDIWPBK-JOCHJYFZSA-N
Catalogs: 4 supplier entries

Core API

The CartBlanche base URL and two reusable helpers used throughout:

import requests, time

BASE = "https://cartblanche22.docking.org"
HEADERS = {"User-Agent": "sciagent-zinc-skill/1.0"}

def get_substance(zinc_id):
    """Fetch full record for one ZINC ID (synchronous)."""
    r = requests.get(f"{BASE}/substance/{zinc_id}.json", headers=HEADERS, timeout=30)
    r.raise_for_status()
    return r.json()

def smiles_lookup(smiles, dist=0, adist=0, database="zinc22", timeout=120, poll=5):
    """Resolve a SMILES to ZINC22 substance dicts via the async task API.

    dist=0, adist=0 -> exact match (fast, reliable; use this).
    dist/adist > 0  -> near-neighbor analog search (slow; may time out — see note).
    Returns a list of substance dicts (possibly empty).
    """
    files = {"smiles": (None, smiles), "dist": (None, str(dist)),
             "adist": (None, str(adist)), "database": (None, database)}
    sub = requests.post(f"{BASE}/smiles.json", files=files, headers=HEADERS, timeout=60)
    sub.raise_for_status()
    task = sub.json()["task"]                       # async: returns {"task": "<uuid>"}

    deadline = time.time() + timeout
    while time.time() < deadline:
        res = requests.get(f"{BASE}/search/result/{task}", headers=HEADERS, timeout=30).json()
        if res.get("status") == "SUCCESS":
            data = res.get("result")
            # result is {"zinc22": [...], "zinc20": [...]} when hits exist,
            # or an empty list when there are none.
            if isinstance(data, dict):
                return data.get("zinc22", []) + data.get("zinc20", [])
            return data or []
        time.sleep(poll)                            # status == "PROGRESS" until done
    raise TimeoutError(f"ZINC22 search task {task} did not finish in {timeout}s")

Query 1: Retrieve a Compound by ZINC ID

Fetch the full record (SMILES, computed properties, formula, ring/heteroatom counts, supplier catalogs) for a known ZINC identifier.

c = get_substance("ZINC000000029632")
td = c["tranche_details"]

print(f"ZINC ID    : {c['zinc_id']}")
print(f"SMILES     : {c['smiles']}")
print(f"Formula    : {c['mol_formula']}")
print(f"MW         : {td['mwt']:.2f}")
print(f"logP       : {td['logp']:.2f}")
print(f"Heavy atoms: {td['heavy_atoms']}")
print(f"Rings      : {c['rings']}   Hetero atoms: {c['hetero_atoms']}")
print(f"InChIKey   : {td['inchikey']}")
print(f"Suppliers  : {len(c.get('catalogs', []))}")

Both the modern ZINCbq.../ZINCbj... style IDs and the legacy 12-digit ZINC000... IDs resolve through this endpoint.

Query 2: Resolve a ZINC ID from a SMILES (Exact Match)

Find the ZINC22 identifier(s) for a molecule you have as a SMILES string. This is the dependable search primitive.

hits = smiles_lookup("CC(=O)Nc1ccc(O)cc1")   # acetaminophen, exact (dist=0)
print(f"Exact matches: {len(hits)}")
for h in hits[:5]:
    td = h["tranche_details"]
    print(f"  {h['zinc_id']:22s}  MW {td['mwt']:.1f}  logP {td['logp']:.2f}  {h['smiles']}")

A single molecule can map to several ZINC IDs across ZINC22 sub-databases (e.g. ZINCbq... and ZINCbj...) — each represents the same structure in a different build.

Query 3: Purchasability and Supplier Catalogs

Purchasability is embedded in the substance record under catalogs — no separate request needed.

c = get_substance("ZINC000000029632")
catalogs = c.get("catalogs", [])
print(f"Supplier entries: {len(catalogs)}")
for cat in catalogs:
    print(f"  {cat.get('catalog_name', 'n/a'):24s} | "
          f"purchasable: {cat.get('purchase')} | "
          f"code: {cat.get('supplier_code', 'n/a')} | "
          f"price: {cat.get('price', 'n/a')} {cat.get('unit', '')}")

purchase: 1 indicates the compound is orderable from that catalog; entries may include price, quantity, and lead time (shipping).

Query 4: Property-Filtered Library (Local RDKit Filtering)

ZINC22 has no server-side MW/logP query. The portable pattern is: gather candidate SMILES (resolved ZINC IDs, a downloaded tranche file, or your own enumeration), then filter locally with RDKit.

import pandas as pd
from rdkit import Chem
from rdkit.Chem import Descriptors, Lipinski

# Candidate SMILES (here: a small hand list; in practice load a tranche .smi or your own set)
smiles_list = [
    "CC(=O)Nc1ccc(O)cc1",            # acetaminophen  (MW 151 - too small, filtered out)
    "CC(C)NCC(O)COc1cccc2ccccc12",   # propranolol    (MW 259, logP 2.6 - lead-like)
    "CN(C)CCOC(c1ccccc1)c1ccccc1",   # diphenhydramine
    "c1ccc(NC(=O)c2ccccc2)cc1",      # benzanilide    (MW 197 - too small, filtered out)
]

def lead_like(smi):
    m = Chem.MolFromSmiles(smi)
    if m is None:
        return None
    mw, logp = Descriptors.MolWt(m), Descriptors.MolLogP(m)
    hbd, hba = Lipinski.NumHDonors(m), Lipinski.NumHAcceptors(m)
    ok = 250 <= mw <= 350 and 1 <= logp <= 3 and hbd <= 3 and hba <= 7
    return {"smiles": Chem.MolToSmiles(m), "mw": round(mw, 1), "logp": round(logp, 2),
            "hbd": hbd, "hba": hba} if ok else None

rows = [r for r in (lead_like(s) for s in smiles_list) if r]
df = pd.DataFrame(rows).drop_duplicates(subset=["smiles"])
print(f"Lead-like compounds after RDKit filter: {len(df)}")
print(df.head())

Analog / Similarity Search (limited — read before relying on it)

The submit endpoint accepts dist (topological graph-edit distance, 0–3) and adist ("anon" distance, scaffold hops) for a near-neighbor analog search:

# Near-neighbor analogs. Keep dist small (1) and the timeout generous; expect FEW results.
try:
    analogs = smiles_lookup("c1ccc(NC(=O)c2ccccc2)cc1", dist=1, adist=0, timeout=240)
    print(f"Analogs: {len(analogs)}")
    for h in analogs[:10]:
        print("  ", h["zinc_id"], h["smiles"])
except TimeoutError as e:
    print("Analog search timed out:", e)

Caveats verified against the live public server:

• dist=0 (exact) is fast and reliable. dist >= 1 runs across billions of molecules on a shared cluster and frequently times out (e.g. dist=3 did not return within ~270 s); even when it succeeds it often returns only the query and a handful of near-identical structures.
• For broad whole-molecule similarity (Tanimoto over graph-edit distance), CartBlanche provides a separate Smallworld search on the website (cartblanche22.docking.org → Similarity → Smallworld, /similarity/sw). It is an interactive flow and is not exposed as a simple GET/POST JSON endpoint here — use the web UI for large similarity jobs.
• Do not build automated pipelines that assume analog search returns a rich set. Base workflows on exact lookup + local RDKit/cheminformatics expansion instead.

Key Concepts

ZINC22 IDs

ZINC22 introduced short alphanumeric IDs such as ZINCbq0000001gLA (the prefix letters encode the sub-database / tranche). Legacy 12-digit IDs like ZINC000000029632 (from ZINC15/ZINC20) still resolve through /substance/{id}.json. Always pass the ID exactly as given.

Asynchronous Search Model

SMILES searches are queued as tasks:

1. POST /smiles.json (multipart form: smiles, dist, adist, database) → {"task": "<uuid>"}
2. GET /search/result/{task} → {"status": "PROGRESS"} while running, then {"status": "SUCCESS", "result": {...}}
3. On success, result is a dict keyed by sub-database (zinc22, sometimes zinc20) when there are hits, or an empty list when there are none. The helper above normalizes both shapes.

Tranches

ZINC organizes molecules into a 2D grid of "tranches" by heavy-atom count / MW (H-codes, e.g. H11) and logP (letter bins). The CartBlanche Tranches browser (cartblanche22.docking.org, "3D"/"2D" tranche pages) lets you select property regions and bulk-download SMILES/SDF for docking. Tranche bulk download is interactive (and large); for programmatic property filtering prefer the RDKit approach in Query 4, or fetch tranche files from files.docking.org and filter locally.

Result Fields

Each search/substance record exposes: zinc_id, smiles, mol_formula, rings, hetero_atoms, db, catalogs[], and tranche_details (mwt, logp, heavy_atoms, inchi, inchikey). SMILES search rows additionally include sub_id, matched_smiles, and the tranche code.

Common Workflows

Workflow 1: Check Purchasability of a Hit List

Goal: Given SMILES of docking hits, resolve their ZINC22 IDs and report which are purchasable.

import pandas as pd

hit_smiles = [
    "CC(=O)Nc1ccc(O)cc1",            # acetaminophen
    "c1ccc(NC(=O)c2ccccc2)cc1",      # benzanilide
]

rows = []
for smi in hit_smiles:
    matches = smiles_lookup(smi)                   # exact
    if not matches:
        rows.append({"query_smiles": smi, "zinc_id": None, "purchasable": False})
        continue
    for m in matches:
        cats = m.get("catalogs", [])
        rows.append({
            "query_smiles": smi,
            "zinc_id": m["zinc_id"],
            "mw": m["tranche_details"]["mwt"],
            "n_catalogs": len(cats),
            "purchasable": any(c.get("purchase") for c in cats) or bool(cats),
        })

df = pd.DataFrame(rows)
print(df)
df.to_csv("hit_purchasability.csv", index=False)
print("Saved: hit_purchasability.csv")

Workflow 2: Resolve and Property-Filter a Candidate Set for Docking

Goal: Take a list of candidate SMILES (your own enumeration or a tranche file), keep the ones present/purchasable in ZINC22, filter to lead-like space, and export SMILES for docking.

import pandas as pd
from rdkit import Chem
from rdkit.Chem import Descriptors

candidates = [
    "CC(=O)Nc1ccc(O)cc1",
    "O=C([C@H]1CCCN1C(=O)Cc1c[nH]c2ccccc12)N1CCc2ccccc2C1",
    "c1ccc(NC(=O)c2ccccc2)cc1",
]

rows = []
for smi in candidates:
    m = Chem.MolFromSmiles(smi)
    if m is None:
        continue
    mw, logp = Descriptors.MolWt(m), Descriptors.MolLogP(m)
    if not (200 <= mw <= 500 and -1 <= logp <= 5):     # lead-like local filter
        continue
    matches = smiles_lookup(smi)                        # confirm it exists in ZINC22
    if not matches:
        continue
    m0 = matches[0]
    rows.append({"zinc_id": m0["zinc_id"], "smiles": Chem.MolToSmiles(m),
                 "mw": round(mw, 1), "logp": round(logp, 2),
                 "purchasable": bool(m0.get("catalogs"))})

df = pd.DataFrame(rows).drop_duplicates(subset=["smiles"])
print(f"ZINC22-confirmed lead-like compounds: {len(df)}")
df[["smiles", "zinc_id"]].to_csv("docking_library.smi", sep=" ", index=False, header=False)
print("Saved: docking_library.smi")
print(df)

Key Parameters

Best Practices

1. Use ZINC22 (CartBlanche), not ZINC15. The zinc15.docking.org REST API is CAPTCHA-walled and will not work from scripts. All endpoints here target cartblanche22.docking.org.

2. Lean on exact lookup; don't depend on analog search. dist=0 is the reliable primitive. For analogs/similarity, expand locally (RDKit, rdkit-cheminformatics) or use the Smallworld web UI — the analog API times out on broad queries.

3. Filter properties locally with RDKit. There is no server-side MW/logP/HBD query in ZINC22. Retrieve candidates, then filter with RDKit — this is also where you apply PAINS/Brenk alerts.

4. Poll politely. SMILES lookups are async; poll /search/result/{task} every few seconds, set a sensible timeout, and handle the PROGRESS → SUCCESS transition (and TimeoutError).

5. Deduplicate by canonical SMILES. A structure can appear under multiple ZINC IDs / catalogs. Canonicalize with RDKit (Chem.MolToSmiles(Chem.MolFromSmiles(smi))) before docking.

6. Verify purchasability via catalogs. Treat catalogs entries with purchase: 1 as orderable; record supplier_code and catalog_name for procurement.

7. Cache results. ZINC data updates periodically; cache substance/search JSON with a date-stamped filename and avoid re-querying within a project.

Common Recipes

Recipe: Lookup ZINC22 ID and purchasability from a SMILES

matches = smiles_lookup("CC(=O)Nc1ccc(O)cc1")   # acetaminophen
for m in matches:
    cats = m.get("catalogs", [])
    print(f"{m['zinc_id']} | MW {m['tranche_details']['mwt']:.1f} | "
          f"catalogs {len(cats)} | purchasable {bool(cats)}")

Recipe: Batch-resolve a list of ZINC IDs

zinc_ids = ["ZINC000000029632", "ZINCbq0000001gLA"]
for zid in zinc_ids:
    try:
        c = get_substance(zid)
        print(f"{zid}: {c['smiles']}  MW {c['tranche_details']['mwt']:.1f}")
    except requests.HTTPError as e:
        print(f"{zid}: not found ({e.response.status_code})")

Recipe: Property Distribution of a Library

import pandas as pd
from rdkit import Chem
from rdkit.Chem import Descriptors

df = pd.read_csv("docking_library.smi", sep=" ", names=["smiles", "zinc_id"])
df["mw"] = df["smiles"].map(lambda s: Descriptors.MolWt(Chem.MolFromSmiles(s)))
df["logp"] = df["smiles"].map(lambda s: Descriptors.MolLogP(Chem.MolFromSmiles(s)))
print(f"Library size: {len(df)}")
print(df[["mw", "logp"]].describe())

Troubleshooting

Related Skills

• rdkit-cheminformatics — Compute properties and apply PAINS/Brenk filters on retrieved ZINC compounds (required for property filtering here)
• autodock-vina-docking — Use exported ZINC SMILES/SDF files for molecular docking campaigns
• chembl-database-bioactivity — Bioactivity data for compounds identified in ZINC virtual screens
• drugbank-database-access — Approved-drug structures and annotations

References

• CartBlanche / ZINC22 web app — Substance lookup, SMILES search, tranche browser
• ZINC22 paper — Tingle et al., "ZINC-22, A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery," J. Chem. Inf. Model. 2023
• docking.org wiki — CartBlanche usage and ZINC22 download documentation
• files.docking.org — Bulk tranche file downloads for ZINC22