# Chemical Space > Navigate make-on-demand catalogues — ZINC-22 through CartBlanche and Enamine REAL Space — to find compounds that can actually be ordered. Use this skill to look substances up by ZINC identifier or structure, understand tranche partitioning by heavy-atom count and logP, and choose between screening an enumerated subset and searching a combinatorial synthon space with a fragment-growing method such as V-SYNTHES. Also trigger on ZINC22, CartBlanche, Enamine REAL, make-on-demand, tangible library, synthon, tranche, giga-scale enumeration, or ultra-large virtual screening. - Skill: `k-dense-ai/chemical-space` (Agent Skill, multi-file: 7 files) - Install (CLI): `npx skillmds@latest add k-dense-ai/chemical-space` - Raw SKILL.md: https://api.skillmd.com/api/skills/k-dense-ai/chemical-space/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: K-Dense AI (https://skillmd.com/u/k-dense-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/k-dense-ai/chemical-space --- # Purchasable Chemical Space Make-on-demand catalogues turned virtual screening from "the million compounds we own" into "the tens of billions a vendor will synthesise". ZINC-22 holds about 54.9 billion 2D structures drawn from Enamine, WuXi, and Mcule; Enamine REAL Space defines around 94 billion combinatorially. This skill is about finding compounds in that space that can actually be ordered — and knowing when the space is too large to enumerate at all. **Services:** `https://cartblanche.docking.org` (per-substance JSON) · `https://files.docking.org/zinc22` (bulk tranche tree). Both unauthenticated. **Checked against:** the live services, August 2026. Read [references/zinc22-and-cartblanche.md](references/zinc22-and-cartblanche.md) before calling the API or downloading a tranche, [references/combinatorial-spaces.md](references/combinatorial-spaces.md) before assuming a space can be enumerated, and [references/screening-strategy.md](references/screening-strategy.md) before designing a cascade — **that one is judgement, not syntax.** ## The two scripts | Script | Answers | |---|---| | `cartblanche_lookup.py` | What is this ZINC id, and can I buy it? | | `space_plan.py` | Which tranches do I need, and what will the screen cost? | ## The API lies about its own responses This is the thing to get right. CartBlanche answers **unknown routes with HTTP 200, a `Content-Type: application/json` header, and its React app's HTML**: | Route | Status | Content-Type | Body | |---|---|---|---| | `/substance/ZINC000000000053.json` | 200 | application/json | **JSON** | | `/substance.json?zinc_id=…` | 200 | application/json | HTML shell | | `/tranches.json` | 200 | application/json | HTML shell | Neither the status code nor the header can be trusted; only the body can. `get_json()` raises a named error when a response begins with `<`. **The one reliable JSON route is `/substance/.json`** — treat CartBlanche as per-identifier lookup and use the file tree for anything bulk. Identifiers are zero-padded to twelve digits: `ZINC53` does not resolve, `ZINC000000000053` does. The scripts accept either. ## Can I buy it? ```bash python skills/chemical-space/scripts/cartblanche_lookup.py substance ZINC19632618 ``` ``` zinc_id smiles mwt logp heavy_atoms purchasable catalogs min_price ZINC000019632618 Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)... 493.615 4.59 37 true 281 240 ``` That is imatinib, offered by 281 catalogues from 240 up. `catalogs` shows supplier, quantity, and lead time per entry. **Existing in ZINC is not the same as being purchasable.** An empty `catalogs` block is a compound that was enumerated but that nobody offers. And a catalogue entry is an *order*, not a vial: make-on-demand synthesis fails roughly one time in five, with lead times of several weeks. ## Which tranches, and what will it cost? The bulk tree is addressed by **heavy-atom count and logP, not molecular weight** — `H25P200` is 25 heavy atoms and logP 2.0, `H25M100` is logP −1.0. A target profile expressed in MW cannot address the directories: ```bash python skills/chemical-space/scripts/space_plan.py tranches --hac-min 18 --hac-max 26 \ --logp-min 1 --logp-max 4 ``` Then make the funnel explicit before committing compute: ```bash python skills/chemical-space/scripts/space_plan.py cascade --library-size 1e9 ``` ``` stage input keep survivors core_hours wall_days_at_cores property filter 1000000000 0.3 300000000 138.9 0.01 fast dock 300000000 0.01 3000000 250000 10.42 standard dock 3000000 0.1 300000 25000 1.04 rescore / MM-GBSA 300000 0.1 30000 25000 1.04 visual triage 30000 0.2 6000 500 0.02 # 1,000,000,000 in, 6,000 out # 300,639 core-hours total; 12.5 days on 1000 cores ``` The property filter is free and removes 70% — always first. Fast docking is 83% of the compute, so that is the only stage where a speedup matters. And 6000 survivors is still far more than anyone buys, so a real campaign ends with diversity selection and a budget cut. ## Above 10⁸ compounds, stop enumerating `strategy` reports the boundary, and the arithmetic behind it is unforgiving: 94 billion SMILES at 100 bytes is about 9 TB before conformers, and docking each for three seconds is roughly 78 000 core-years. A combinatorial space is reagents plus reaction rules, not a list — so search it without enumerating it. Dock a minimal fragment library covering every scaffold and synthon, keep the best, and enumerate only those. V-SYNTHES2 reports this over 36 billion REAL Space compounds. ## Four ways this misleads 1. **Bigger is sublinearly better.** A thousandfold larger library buys closer to one log of affinity, and it does not improve enrichment — false positives scale with N too. 2. **Giga-scale finds your scoring function's blind spots reliably.** High molecular weight, over-buried hydrophobics, and strained conformers presented as favourable are systematic artefacts, and a thorough search finds them precisely because it is thorough. 3. **Receptor quality does not scale away.** A wrong protonation state costs the same fraction of the answer at any N. Preparing the structure beats enlarging the library. 4. **REAL Space is narrower than its size suggests.** It is a product over amide couplings, Suzukis, and reductive aminations; unusual scaffolds are systematically absent. ## When to stop using these tools For similarity and substructure search across REAL Space without enumeration, use SmallWorld () and Arthor (). For the full synthon definition of REAL Space you need an agreement with Enamine — identifiers are public, the reagent and reaction files are not. ## Composing with the rest of the bundle - `binding-site-analysis` → before: is the pocket worth a billion compounds at all? - `medchem` / `rdkit` → before: property filters and PAINS removal, at the top of the funnel. - `autodock-vina` → alongside: the docking engine the cascade is costed for. - `retrosynthesis` → alongside: for anything not in a catalogue, can it be made? - `chembl` → after: is a hit series already known against this target? ## Reporting results honestly Give the library actually screened, the keep-fraction at every stage, and whether a redocking control reproduced a known pose. A docking score is not an affinity and a rank is not a potency prediction. Report compounds ordered *and* compounds received — a hit rate computed against orders rather than deliveries is wrong by about a fifth.