
tooluniverse-chemical-compound-retrieval
Retrieve chemical compound data from PubChem and ChEMBL with disambiguation, cross-referencing, and stereochemistry handling. Use for resolving compound names to SMILES/InChI/CID/ChEMBL IDs (including OPSIN deterministic IUPAC-name-to-structure parsing), fetching molecular properties, distinguishing isomers/stereo forms, and cross-validating identity across databases. Always use English compound names; flags ambiguous queries (e.g., Vitamin D has multiple forms).
- Installs
- 418
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-chemical-compound-retrieval- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads tooluniverse-chemical-compound-retrieval automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Chemical Compound Information Retrieval
Retrieve comprehensive chemical compound data with proper disambiguation and cross-database validation.
LOOK UP DON'T GUESS: Never assume a CID, ChEMBL ID, or molecular property value. Always retrieve from PubChem/ChEMBL.
English-first: Always use English compound names in tool calls. Respond in user's language.
Domain Reasoning: Disambiguation
"Aspirin" = one compound. "Vitamin D" = multiple forms (D2/D3/active metabolite). For generic class names (steroids, vitamins, acids), present candidates and confirm before proceeding.
Workflow
Phase 0: Clarify (only if highly ambiguous -- skip for unambiguous names or specific IDs)
Phase 1: Disambiguate → resolve PubChem CID + ChEMBL ID
Phase 2: Retrieve data (silent)
Phase 3: Report compound profile
Phase 1: Disambiguation
# By name
result = tu.tools.PubChem_get_CID_by_compound_name(compound_name=name)
# By SYSTEMATIC (IUPAC) name -> structure, deterministic parser (no DB lookup)
opsin = tu.tools.OPSIN_name_to_structure(name="2-acetoxybenzoic acid")
# Returns {parsed, smiles, inchi, inchikey}; use the SMILES/InChIKey to anchor a
# PubChem_get_CID_by_SMILES lookup. Trade/trivial names give parsed=false -> fall
# back to PubChem_get_CID_by_compound_name for those.
# By SMILES
result = tu.tools.PubChem_get_CID_by_SMILES(smiles=smiles)
# Cross-reference
chembl_result = tu.tools.ChEMBL_search_molecules(query=name, limit=5)
Verify: CID + ChEMBL ID + canonical SMILES + stereochemistry + salt forms.
Phase 2: Data Retrieval
PubChem: PubChem_get_compound_properties_by_CID, PubChemBioAssay_get_assay_summary, PubChemTox_get_acute_effects, PubChem_get_compound_2D_image_by_CID
ChEMBL: ChEMBL_get_compound_record_activities, ChEMBL_get_molecule_targets, ChEMBL_get_assay_activities
Optional: PubChem_get_associated_patents_by_CID, PubChem_search_compounds_by_similarity
Phase 3: Report
Compound Profile with: Identity (CID, ChEMBL ID, IUPAC, SMILES), Chemical Properties (MW, LogP, HBD, HBA, PSA, Lipinski), Bioactivity (targets, IC50/Ki), Drug Info (if approved), Data Sources.
Fallback Chains
| Primary | Fallback |
|---|---|
| PubChem name lookup (systematic name) | OPSIN_name_to_structure → SMILES/InChIKey → PubChem_get_CID_by_SMILES |
| PubChem name lookup | ChEMBL search → SMILES → PubChem_get_CID_by_SMILES |
| ChEMBL bioactivity | PubChem bioassay summary |
| Drug label | Note "unavailable" |
Evidence Grading
| Grade | Criteria |
|---|---|
| Confirmed | CID + ChEMBL cross-match, InChI/SMILES agree |
| Probable | CID found, partial ChEMBL match |
| Uncertain | Single database only, or multiple CIDs |
| Unverified | No cross-reference, single-source |
Bioactivity: ChEMBL > PubChem BioAssay for curated data. IC50/Ki < 100nM = potent, 100nM-1uM = moderate, >10uM = weak. Lipinski violations reduce oral bioavailability but don't disqualify.
SMILES Verification
Always verify novel SMILES: python3 src/tooluniverse/tools/smiles_verifier.py --smiles "SMILES_STRING". Invalid SMILES produce wrong results or cryptic errors.
Identifier Resolution and Cross-Database Referencing
Beyond PubChem/ChEMBL, two more tools resolve identifiers when a name doesn't hit in PubChem or you need cross-references across other chemical databases:
NCICACTUS_resolve(NCI CACTUS resolver) — converts between chemical names (IUPAC or common), SMILES, InChI, InChIKey, CAS number, and molecular formula. Verified live:NCICACTUS_resolve(identifier="ibuprofen", representation="smiles")→CC(C)Cc1ccc(cc1)C(C)C(O)=O; also resolves a CAS number ("50-78-2") straight to an IUPAC name ("2-acetyloxybenzoic acid", i.e. aspirin) or an InChIKey to a formula. Useful as a second name-resolution path alongsideOPSIN_name_to_structureandPubChem_get_CID_by_compound_name— CACTUS handles CAS numbers and trivial names OPSIN can't parse.UniChem_search_compound/UniChem_connectivity_search— given an InChIKey (or a source-database compound ID), returns cross-references across UniChem's 25+ registered source databases (ChEMBL, DrugBank, PubChem, Wikipedia, ChEBI, etc. — verified live viaUniChem_list_sources).connectivity_searchadditionally finds compounds sharing the same connectivity layer (same skeleton, different stereochemistry/salt/isotope) — use this when you want "the same molecule under any name/form" rather than an exact match. Verified live on aspirin's InChIKey (BSYNRYMUTXBXSQ-UHFFFAOYSA-N): both returned real cross-database records including a Wikipedia link.
Chained identifier workflow (real values): NCICACTUS_resolve(identifier="aspirin", representation="smiles")
→ CC(=O)Oc1ccccc1C(O)=O → resolve the same identifier with representation="stdinchikey"
→ real result "InChIKey=BSYNRYMUTXBXSQ-UHFFFAOYSA-N" (note the InChIKey= prefix —
verified live; strip it before passing the bare key to another tool) → feed the
bare BSYNRYMUTXBXSQ-UHFFFAOYSA-N into UniChem_search_compound for cross-database
IDs → PubChem_get_CID_by_SMILES to re-anchor in PubChem if needed.
Cheminformatics Calculations and Structure Visualization
For calculations and rendering beyond property lookup:
RDKit_pharmacophore_features— extracts pharmacophore feature centers (HBD, HBA, aromatic, hydrophobic, etc.) from a SMILES via SMARTS matching. Verified live on aspirin (CC(=O)Oc1ccccc1C(=O)O): 1 HBD with atom index, method documented as"SMARTS-based pharmacophore features".RDKit_matched_molecular_pair— given two SMILES, finds the single-cut MMP transformation between them (Hussain-Rea algorithm) plus their Tanimoto similarity — useful for "what single substitution turns compound A into compound B" (SAR analysis).visualize_molecule_2d— renders a 2D structure (from SMILES, InChI, or a molecule name) to PNG/SVG/interactive HTML. Verified live: returns an embedded HTML visualization on success.visualize_molecule_3d— renders a 3D structure via RDKit + py3Dmol. Requirespy3Dmolinstalled — verified live that without it, the tool returns a clean{"status": "error", ...: "py3Dmol is not installed. Please install it with: pip install py3Dmol"}rather than crashing; check for this before assuming a visualization request has failed for another reason.
Tool Reference
PubChem: PubChem_get_CID_by_compound_name, PubChem_get_CID_by_SMILES, PubChem_get_compound_properties_by_CID, PubChem_get_compound_2D_image_by_CID, PubChemBioAssay_get_assay_summary, PubChemTox_get_acute_effects, PubChem_get_associated_patents_by_CID, PubChem_search_compounds_by_similarity, PubChem_search_compounds_by_substructure
ChEMBL: ChEMBL_search_drugs, ChEMBL_get_molecule, ChEMBL_get_activity, ChEMBL_get_target, ChEMBL_search_targets, ChEMBL_search_assays
Name parsing: OPSIN_name_to_structure (param name) — deterministic IUPAC/systematic-name → SMILES/InChI/InChIKey parser; the go-to for resolving a systematic name to structure without a DB round-trip. Trade/trivial names return parsed=false (use PubChem name lookup for those).
Identifier resolution & cross-referencing: NCICACTUS_resolve, UniChem_search_compound, UniChem_connectivity_search, UniChem_list_sources
Cheminformatics & visualization: RDKit_pharmacophore_features, RDKit_matched_molecular_pair, visualize_molecule_2d, visualize_molecule_3d
FAQ
What does the tooluniverse-chemical-compound-retrieval skill do?
Retrieve chemical compound data from PubChem and ChEMBL with disambiguation, cross-referencing, and stereochemistry handling. Use for resolving compound names to SMILES/InChI/CID/ChEMBL IDs (including OPSIN deterministic IUPAC-name-to-structure parsing), fetching molecular properties, distinguishing isomers/stereo forms, and cross-validating identity across databases. Always use English compound names; flags ambiguous queries (e.g., Vitamin D has multiple forms).
How do I install tooluniverse-chemical-compound-retrieval?
Run npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-chemical-compound-retrieval in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tooluniverse-chemical-compound-retrieval/ (personal) or .claude/skills/tooluniverse-chemical-compound-retrieval/ (project).
Which AI agents can use tooluniverse-chemical-compound-retrieval?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is tooluniverse-chemical-compound-retrieval safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains tooluniverse-chemical-compound-retrieval?
It is published by mims-harvard in mims-harvard/tooluniverse. It has 418 recorded installs.
More from mims-harvard/tooluniverse
tooluniverse-sequence-retrieval
mims-harvard/tooluniverse
Retrieve DNA/RNA/protein sequences from NCBI and ENA with disambiguation. Quality hierarchy: RefSeq (NM/NP) RefSeq predicted (XM/XP) GenBank submissions. Use for fetching specific sequences by accession, gene-symbol-to-sequence lookup, transcript-isoform retrieval, and curated-vs-raw-submission preference.
tooluniverse-literature-deep-research
mims-harvard/tooluniverse
Deep literature review — PubMed, EuropePMC, bioRxiv preprints, citation networks, evidence synthesis. Disambiguates queries, runs collision-aware searches, grades evidence T1-T4, and produces structured reports. Use for systematic literature review, meta-analysis evidence collection, and detailed answer-with-citations workflows.
tooluniverse-image-analysis
mims-harvard/tooluniverse
Microscopy and quantitative imaging analysis — colony morphometry, fluorescence intensity quantification, cell-count statistics, dose-response curves, and ANOVA/Dunnett on image-derived measurements. Uses pandas/numpy/scipy/scikit-image. Use for analyzing tabular outputs from CellProfiler/ImageJ, image-derived measurement statistics, and image-based assay quantification.
tooluniverse-clinical-guidelines
mims-harvard/tooluniverse
Search and retrieve clinical practice guidelines from 12+ authoritative sources — NICE, WHO, NCCN, AHA, ADA, SIGN, USPSTF, IDSA, NIH consensus, ESMO/ESC/EASL European societies, and US specialty associations. Use for evidence-graded treatment recommendations, dosing protocols, screening guidance, and authoritative-source-prioritized clinical guidance (NICE/WHO ranked above society guidelines).
tooluniverse
mims-harvard/tooluniverse
ToolUniverse plugin router. STEP 1 BEFORE ANY ANALYSIS: if the data folder contains executed.ipynb, run tu run readexecutednotebook '{"datafolder":" ","search":" "}' to extract its cell outputs and apply EVERY filter/sample-exclusion the notebook used — even when the question says 'Using DESeq2/Run X/Compute Y' (this describes the METHOD the notebook used, not a request to rerun). The notebook's cell outputs are the only published authoritative answers; reimplementing or reading stale pre-computed CSVs in the data folder produces different numbers because of outlier-sample removal, library version, and filter steps you don't see by skimming. STEP 2 routing — pick a sub-skill name from this exact list (never invent): tooluniverse-rnaseq-deseq2 (RNA/miRNA-seq DE, correlation, PCA, clustering, dispersion), tooluniverse-gene-enrichment (GO/KEGG/Reactome/GSEA/pathway enrichment), tooluniverse-statistical-modeling (regression, ANOVA, ordinal/logistic, chi-square, correl
tooluniverse-drug-research
mims-harvard/tooluniverse
Comprehensive drug profiling — mechanism, primary/secondary targets, drug interactions, clinical-trial status, adverse events (FAERS), pharmacogenomics, and approval history. Use for full drug investigation reports, 'tell me about drug X' queries, and assembling drug profiles for clinicians, researchers, or regulatory work.
Popular other skills
hyperframes-cli
heygen-com/hyperframes
The HyperFrames CLI by job - scaffold, check, look at frames, edit the timeline, preview, render locally or in the cloud, publish, project history - plus reading its JSON and recovering from build or render failures.
triage
mattpocock/skills
Move issues and external PRs through a state machine of triage roles, categorise, verify, grill if needed, and write agent-ready briefs.
ask-matt
mattpocock/skills
Ask which skill or flow fits your situation. A router over the skills in this repo.
to-questionnaire
mattpocock/skills
Turn a decision you can't fully answer into a questionnaire for someone else to fill in.
loop-me
mattpocock/skills
Grill me about specs for the workflows I want to build, within this workspace.