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Agent skill

Drug DB Chembl

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Query ChEMBL web services for targets, molecules, and curated bioactivity measurements (IC50, Ki, EC50, etc.).

MITAuto-check passedResearch & Science

Install Drug DB Chembl

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-db-chembl -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills drug-db-chembl --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/drug-db-chembl .claude/skills/drug-db-chembl && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
drug-db-chembl
GitHub stars
176
Token cost
~1.4k tokens
SKILL.md length
340 words
Files
5 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Query ChEMBL web services for targets, molecules, and curated bioactivity measurements (IC50, Ki, EC50, etc.).

  • Works in 6 steps: Search for candidate targets by name… → Resolve target by UniProt accession… → Retrieve bioactivity data for a target… → …
  • Tasks that involve Drug discovery and cheminformatics
  • SKILL.md covers Goal, Instructions, Examples and Constraints
  • Runs Python scripts from its folder

What it does

Drug DB Chembl is an agent skill from learningmatter-mit/AtomisticSkills. Query ChEMBL web services for targets, molecules, and curated bioactivity measurements (IC50, Ki, EC50, etc.).

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts (for example `examples/README.md`, `examples/egfr_ic50_activities.json` and `examples/egfr_targets.json`).

It sits in Research & Science, covering Drug discovery and cheminformatics. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

When your agent uses it

  • Tasks that involve Drug discovery and cheminformatics

Example prompts

  • “/drug-db-chembl”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Search for candidate targets by name (broad recall)
  2. Resolve target by UniProt accession (higher precision)
  3. Retrieve bioactivity data for a target (recommended "model-ready" defaults)
  4. Look up a molecule record (ChEMBL ID or InChIKey)
  5. Chemical search by SMILES (similarity or substructure)
  6. Export as CSV for quick inspection

What it can do on your machine

Read from SKILL.md and the folder at commit 6257444. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Drug DB Chembl loads about 1.4k tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 340 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~31
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from learningmatter-mit/AtomisticSkills at commit 6257444, republished under its MIT licence (© learningmatter-mit). 340 words, ~1,354 tokens.

Download SKILL.mdSave it as .claude/skills/drug-db-chembl/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
drug-db-chembl
description
Query ChEMBL web services for targets, molecules, and curated bioactivity measurements (IC50, Ki, EC50, etc.).
metadata.category
drug-discovery
metadata.venv
cpu

db-chembl

Goal

To programmatically query the ChEMBL database web services and retrieve reproducible, model-ready datasets of targets, molecules, and bioactivities, while preserving provenance (assay/document IDs) and enabling common curation filters (e.g., pChEMBL, standardized units, handling censoring operators, assay type).

ChEMBL activity data is curated and standardized, but downstream modeling still requires careful selection/filters to avoid mixing incompatible assay formats or censored measurements.

Instructions

1. Search for candidate targets by name (broad recall)

Use this when you only have a gene/protein string and want candidate ChEMBL target IDs.

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_chembl.py \
  --target "EGFR" \
  --max_results 20 \
  --output egfr_targets.json
2. Resolve target by UniProt accession (higher precision)

If you know a UniProt accession, this reduces ambiguity compared to free-text searching.

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_chembl.py \
  --uniprot "P00533" \
  --target_type "SINGLE PROTEIN" \
  --max_results 10 \
  --output egfr_targets_uniprot.json

ChEMBL web services are paginated (limit/offset + page_meta); this script automatically iterates pages up to --max_results.

Recommended for many QSAR/ML use cases:

  • use standardized fields (standard_*)
  • prefer binding assays (--assay_type B) when you want binding potency
  • restrict to equality relations (--standard_relation "=") to avoid mixing censored labels
  • restrict to nM for consistency (--standard_units nM)
  • require/compute pChEMBL (comparable negative log molar potency)
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_chembl.py \
  --target_id "CHEMBL203" \
  --activity_type "IC50" \
  --assay_type "B" \
  --standard_relation "=" \
  --standard_units "nM" \
  --require_pchembl \
  --pchembl_min 5.0 \
  --max_results 200 \
  --output egfr_ic50_pchembl.json
4. Look up a molecule record (ChEMBL ID or InChIKey)

Prefer ChEMBL ID or InChIKey for exact identity.

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_chembl.py \
  --chembl_id "CHEMBL25" \
  --output aspirin_record.json
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_chembl.py \
  --inchi_key "BSYNRYMUTXBXSQ-UHFFFAOYSA-N" \
  --output aspirin_record_by_inchikey.json
5. Chemical search by SMILES (similarity or substructure)

SMILES strings often differ by canonicalization; similarity/substructure search is usually more robust than "exact SMILES match."

Similarity search (default cutoff 70):

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_chembl.py \
  --smiles "CC(=O)Oc1ccccc1C(=O)O" \
  --smiles_mode similarity \
  --similarity 80 \
  --max_results 10 \
  --output aspirin_similarity.json

Substructure search:

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_chembl.py \
  --smiles "CC(=O)Oc1ccccc1C(=O)O" \
  --smiles_mode substructure \
  --max_results 10 \
  --output aspirin_substructure.json
6. Export as CSV for quick inspection
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_chembl.py \
  --target_id "CHEMBL203" \
  --activity_type "IC50" \
  --assay_type "B" \
  --standard_relation "=" \
  --standard_units "nM" \
  --require_pchembl \
  --max_results 200 \
  --output egfr_ic50_pchembl.csv

Examples

EGFR binding-potency dataset (IC50) with comparable pChEMBL values:

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_chembl.py --target "EGFR" --max_results 10 --output egfr_targets.json

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_chembl.py \
  --target_id "CHEMBL203" \
  --activity_type "IC50" \
  --assay_type "B" \
  --standard_relation "=" \
  --standard_units "nM" \
  --require_pchembl \
  --pchembl_min 5.0 \
  --max_results 200 \
  --output egfr_ic50_pchembl.json

Constraints

  • Pagination: ChEMBL web services return results in pages (limit/offset) with page_meta. Use --max_results to cap downloads.
  • Rate limiting / etiquette: This script inserts a small delay between requests; you can adjust it with --delay.
  • Data comparability:
    • Prefer standardized fields (standard_type/value/units/relation) and pChEMBL for comparable potency where appropriate.
    • Do not mix censored relations (>, <) into regression labels unless you explicitly model censoring.
  • Target mapping confidence: ChEMBL assigns a 0-9 confidence score to assay-to-target mappings; consider using it when building high-precision datasets.
  • Environment: Requires the cpu environment.
  • Dependencies: Standard library only (urllib, json, csv, etc.).

Author: Matthew Cox Contact: GitHub @mcox3406

© learningmatter-mit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts) in skills/drug-db-chembl of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/README.md
  • examples/egfr_ic50_activities.json
  • examples/egfr_targets.json
  • scripts/query_chembl.py

Open the folder on GitHubat commit 6257444

Compare with similar skills

Drug DB Chembl next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Drug DB Chembl compared with similar skills
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Drug DB Chembl this skilllearningmatter-mit/AtomisticSkills176—~1.4kAutomated safety check: PassMIT
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Drug DiscoveryTommy-yw/RunbookHermes5461 repos~2.3kAutomated safety check: PassMIT
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills48k1 repos~3kAutomated safety check: NotesMIT
Biomedical Analysis Dispatchxjtulyc/MedgeClaw6171 repos~2kAutomated safety check: PassNone
Edu Chem Reactionwy51ai/edulab1.4k—~1.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Drug DB Chembl

What does Drug DB Chembl do?

Query ChEMBL web services for targets, molecules, and curated bioactivity measurements (IC50, Ki, EC50, etc.). Drug DB Chembl is an agent skill from learningmatter-mit/AtomisticSkills.).

When should I use Drug DB Chembl?

Drug DB Chembl fits situations like: tasks that involve Drug discovery and cheminformatics.

How do I install Drug DB Chembl in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-db-chembl -a claude-code`. Or copy the skill folder (skills/drug-db-chembl in learningmatter-mit/AtomisticSkills) into .claude/skills/drug-db-chembl in your project. Claude Code loads it when a task matches its description.

How do I install Drug DB Chembl in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-db-chembl -a codex`. Or copy the skill folder (skills/drug-db-chembl in learningmatter-mit/AtomisticSkills) into .agents/skills/drug-db-chembl in your project. Codex loads it when a task matches its description.

Can I use Drug DB Chembl in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-db-chembl -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drug-db-chembl, .gemini/skills/drug-db-chembl, .github/skills/drug-db-chembl and .opencode/skills/drug-db-chembl in your project.

What does Drug DB Chembl need to run?

Going by SKILL.md and its folder, Drug DB Chembl needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Drug DB Chembl access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Drug DB Chembl safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Drug DB Chembl use?

Drug DB Chembl is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Drug DB Chembl use?

About 1.4k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Drug DB Chembl?

Skills that share tags, products or a category with Drug DB Chembl: Molecode (AtomFlow-AI/MoleCode, 305 stars), Drug Discovery (Tommy-yw/RunbookHermes, 546 stars), DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars) and Biomedical Analysis Dispatch (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drug DB Chembl?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 2026.

Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.