Patent Landscape logo

Patent Landscape

Organization
K-Dense-AI
patent-landscape

Find out whether a chemical series is already claimed, using SureChEMBL's patent-extracted compound corpus and, where a key is available, PatentsView for legal status and assignee history. Use this skill to trace a structure to the patent documents that disclose it, survey an assignee's filings around a target, and understand what the freedom-to-operate question requires that a structure search cannot answer. Also trigger on SureChEMBL, patent chemistry, Markush structure, freedom to operate, composition of matter, assignee, priority date, patent family, or PatentsView.

Overview

PublisherK-Dense-AI
Repositorydrug-discovery-agent-skills
Skill namepatent-landscape
Stars
51
Forks
5
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by K-Dense-AI on GitHub. Read the source before you install it.

Installation

Install the Patent Landscape AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/K-Dense-AI/drug-discovery-agent-skills/tree/main/skills/patent-landscape
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/K-Dense-AI/drug-discovery-agent-skills.git /tmp/drug-discovery-agent-skills
mkdir -p .claude/skills
cp -r /tmp/drug-discovery-agent-skills/skills/patent-landscape .claude/skills/patent-landscape
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Patent Landscape in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Patent Landscape on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Patent Landscape is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Chemical Patent Landscape

Whether a series is already claimed decides whether it is worth pursuing, and the question comes up long before anyone talks to an attorney. SureChEMBL extracts structures from patent full text, images, and attachments — over 31 million compounds from the major offices — and it is the only open, structure-searchable patent chemistry resource.

Services: https://ftp.ebi.ac.uk/pub/databases/chembl/SureChEMBL (bulk, no key) · https://search.patentsview.org/api/v1 (optional, free key). Checked against: the live bulk tree, August 2026 — 31 releases, newest 2026-08-04.

Read references/surechembl-bulk.md before downloading anything, references/patent-data-sources.md to know which source is silent about what, and references/reading-patents.md before drawing any conclusion — that one is judgement, not syntax, and the gap it describes is wide.

The two scripts

ScriptAnswers
surechembl_bulk.pyWhich release, which tables, and how much disk?
patent_search.pyWho is filing in this space, in the US?

SureChEMBL has no REST API

Checked live: surechembl.org/api/ and every plausible variant return 404. The website is interactive-only, and everything programmatic goes through the EBI bulk tree.

bash
python skills/patent-landscape/scripts/surechembl_bulk.py releases --limit 3
# 31 releases, newest 2026-08-04. Updated fortnightly.
release     url
2026-08-04  https://ftp.ebi.ac.uk/.../bulk_data/2026-08-04/
2026-07-17  ...

Two directories hold different data. bulk_data/ is SureChEMBL 2.0 — Parquet plus an FPSim2 similarity index, fortnightly. data/ is the legacy quarterly txt/SDF dump whose README is dated 2016. They are easily confused and the old one is much less useful.

Pin a release. Taking "latest" makes an analysis irreproducible against a corpus that changes every two weeks.

Plan the download; it is 15 GB

bash
python skills/patent-landscape/scripts/surechembl_bulk.py plan --question structure-to-patent
# structure-to-patent: match a structure to compound ids, then to the documents disclosing it
# 3 table(s), 14.1 GB for release 2026-08-04
curl -O https://ftp.ebi.ac.uk/.../compounds.parquet           # 4.2 GB
curl -O https://ftp.ebi.ac.uk/.../patent_compound_map.parquet # 5.0 GB
curl -O https://ftp.ebi.ac.uk/.../patents.parquet             # 5.9 GB

A similarity search needs only fpsim2_fingerprints.h5 (1.4 GB) and compounds.parquet. Query the Parquet with DuckDB rather than pandas — it reads them in place without loading them.

Where the compound was found is the legal signal

patent_compound_map.parquet records which document field each compound came from, and that column carries almost all the meaning:

FieldWhat it usually means
claimsthe compound is claimed — the one that matters
title / abstracta headline compound of the filing
descriptiondisclosed: possibly prior art, a comparator, or a reagent
imageextracted from a drawing by OCSR, and sometimes wrong

Treating every extracted compound as "claimed by this patent" is the commonest misreading of this dataset. Only the claims define a monopoly.

Markush claims are not enumerated

This is the limitation that matters most. Chemical patents claim a genus — a scaffold with variable positions — and a single Markush claim can cover billions of compounds. SureChEMBL extracts the specific examples, not the genus.

So no structure hit means this exact structure was not disclosed as an example. It does not mean the structure falls outside every claim, and for a novel analogue of a known series the opposite is usually true. Markush search is a specialist capability that Reaxys, SciFinder, and Derwent implement and no free source does.

Novelty and freedom to operate are different questions

Novelty — has this been disclosed before? — determines whether you can patent it. A structure search genuinely helps.

Freedom to operate — can I sell this without infringing? — requires reading the claims of every in-force patent in every jurisdiction you will sell in, construed against your product. A structure search does not answer this and cannot.

They are independent: a compound can be novel and infringing, or old and non-infringing.

The US-only half

bash
python skills/patent-landscape/scripts/patent_search.py assignees --title "PROTAC"

Needs a free PATENTSVIEW_API_KEY. Note that a missing key surfaces as a connection failure rather than a 401, so the obvious diagnosis is a network problem; the script checks explicitly.

Two coverage facts to carry: PatentsView is US grants and pre-grant publications only — no EPO, WIPO, CNIPA, or JPO — and assignee names are not normalised, so "Merck", "Merck Sharp & Dohme", and "Merck & Co., Inc." count as three companies.

The 18-month blind spot

Applications publish 18 months after priority. Everything filed in the last 18 months is invisible in every source, without exception. An empty landscape may mean nobody is working on the target, or that everybody filed last year — and the second is common precisely for the targets worth working on.

Composing with the rest of the bundle

  • chembl → here: SureChEMBL ids cross-reference through UniChem, joining patent chemistry to measured bioactivity.
  • chemical-space → alongside: purchasable is a different question from unclaimed.
  • generative-design → after: novelty of generated structures is a real use for this.
  • clinicaltrials / openfda → alongside: filings, trials, and approvals are three views of the same competitive picture.

Reporting results honestly

Name the source, its coverage, and the pinned release. Say which document field a compound was found in. State that Markush claims are not enumerated and that the search under-reports coverage for that reason. Note the 18-month lag. Give family counts rather than document counts where you can.

Never write "this compound is free to use". Write "no exact structure match in SureChEMBL release 2026-08-04; this does not address Markush claims, unpublished applications, or claim construction, and is not a freedom-to-operate assessment." Any decision with money attached needs a patent attorney — this skill exists to tell you whether to go and ask one.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Patent Landscape AI skill do?

Find out whether a chemical series is already claimed, using SureChEMBL's patent-extracted compound corpus and, where a key is available, PatentsView for legal status and assignee history. Use this skill to trace a structure to the patent documents that disclose it, survey an assignee's filings around a target, and understand what the freedom-to-operate question requires that a structure search cannot answer. Also trigger on SureChEMBL, patent chemistry, Markush structure, freedom to operate, composition of matter, assignee, priority date, patent family, or PatentsView.

Why use Patent Landscape on TypingMind?

Because you install it once and use it with any model. Patent Landscape is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Patent Landscape in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/K-Dense-AI/drug-discovery-agent-skills/tree/main/skills/patent-landscape. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Patent Landscape?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Patent Landscape?

As many as you like. As long as a model supports skills, you can use Patent Landscape with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Patent Landscape AI skill free?

Yes. It is published on GitHub by K-Dense-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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