Tooluniverse Small Molecule Discovery

KEY PRINCIPLES:

1. Resolve identity first - Always get CID and ChEMBL ID before research
2. SMILES required for property prediction - Extract canonical SMILES from PubChem early
3. English names in tools - Use IUPAC or common English names; avoid abbreviations in tool calls
4. BindingDB is often unavailable - Fall back to ChEMBL activities when BindingDB times out
5. eMolecules/Enamine return URLs - These tools generate search URLs, not direct data; note this to user

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

When to Use

• "Find information about compound X"
• "What is the drug-likeness of this SMILES?"
• "Show binding affinities for EGFR inhibitors"
• "Search for compounds similar to imatinib"
• "Is this compound commercially available?"
• "What are the ADMET properties of this molecule?"
• "Find ChEMBL activities for target Y"
• "Predict targets for this small molecule"

Key Tools

Workflow

Phase 1: Compound Identification

# Step 1: Name -> CID (PubChem canonical identity)
PubChem_get_CID_by_compound_name(compound_name="imatinib")
# -> CID: 5291

# Step 2: Get SMILES and properties (needed for all downstream tools)
PubChem_get_compound_properties_by_CID(
    cid="5291",
    properties="MolecularFormula,MolecularWeight,CanonicalSMILES,InChIKey,IUPACName"
)
# -> canonical SMILES, InChIKey (global identifier)

# Step 3: Get ChEMBL ID (for activity data)
ChEMBL_search_molecules(query="imatinib")
# -> ChEMBL ID (e.g., "CHEMBL941")

# Step 4: Get all synonyms (brand names, INN, etc.)
PubChem_get_compound_synonyms_by_CID(cid="5291")

ID resolution priority:

1. Start with PubChem CID (most universal)
2. Get ChEMBL ID (for bioactivity data)
3. Use canonical SMILES for structure-based searches and ADMET

Phase 2: Structure-Based Search

Similarity search (find analogs):

PubChem_search_compounds_by_similarity(
    smiles="CANONICAL_SMILES",
    threshold=85   # Tanimoto threshold 0-100; 85 = highly similar
)
# Returns: list of CIDs of similar compounds

ChEMBL_search_similar_molecules(query="CHEMBL941")  # Or SMILES
# Returns: ChEMBL entries sorted by similarity

Substructure search (find compounds containing a scaffold):

PubChem_search_compounds_by_substructure(smiles="SCAFFOLD_SMILES")
# Returns: CIDs of compounds containing the scaffold

Phase 3: Bioactivity and Binding Affinity

Get all activities for a compound (across all targets):

ChEMBL_search_activities(
    molecule_chembl_id="CHEMBL941",
    pchembl_value__gte=6,   # pIC50/Ki >= 6 = IC50/Ki <= 1 µM
    limit=50
)
# Returns: assay_type, target_name, pchembl_value, units

Get all ligands for a target:

# First find target ChEMBL ID
ChEMBL_search_targets(query="EGFR", organism="Homo sapiens")
# -> target_chembl_id, e.g., "CHEMBL203"

ChEMBL_get_target_activities(
    target_chembl_id="CHEMBL203"
)
# Returns: all compounds with binding data against this target

BindingDB (when available — often times out):

BindingDB_get_ligands_by_uniprot(uniprot_id="P00533")  # EGFR
# Returns: Ki, IC50, Kd data with literature references
# Note: BindingDB REST API is frequently unavailable; fall back to ChEMBL

pChEMBL Value interpretation:

Phase 4: Drug-likeness and ADMET

SwissADME (comprehensive, requires SMILES string — not list):

SwissADME_calculate_adme(
    operation="calculate_adme",
    smiles="CANONICAL_SMILES"
)
# Returns: physicochemical, lipophilicity, water solubility, pharmacokinetics,
#          drug-likeness scores (Lipinski, Veber, Egan, Muegge), PAINS alerts

SwissADME_check_druglikeness(
    operation="check_druglikeness",
    smiles="CANONICAL_SMILES"
)
# Returns: Lipinski/Veber/Egan pass/fail + lead-likeness

ADMET-AI (ML-based, requires SMILES as list — install tooluniverse[ml]):

ADMETAI_predict_physicochemical_properties(smiles=["CANONICAL_SMILES"])
ADMETAI_predict_bioavailability(smiles=["CANONICAL_SMILES"])
ADMETAI_predict_BBB_penetrance(smiles=["CANONICAL_SMILES"])
ADMETAI_predict_toxicity(smiles=["CANONICAL_SMILES"])
ADMETAI_predict_CYP_interactions(smiles=["CANONICAL_SMILES"])

Note: ADMET-AI requires pip install tooluniverse[ml]. If unavailable, use SwissADME as fallback.

Key drug-likeness rules:

• Lipinski Ro5: MW <= 500, logP <= 5, HBD <= 5, HBA <= 10 (oral drugs)
• Veber: TPSA <= 140 Ų, rotatable bonds <= 10 (oral bioavailability)
• Lead-like: MW <= 350, logP <= 3, HBD <= 3, HBA <= 6 (fragment/lead)

Phase 5: Target Prediction

When you have a novel compound and want to predict targets:

SwissTargetPrediction_predict(
    operation="predict",
    smiles="CANONICAL_SMILES"
)
# Returns: predicted protein targets with probability scores
# Note: SwissTargetPrediction uses structure-similarity to known drug-target pairs
# May time out for complex molecules

Phase 6: Commercial Availability

eMolecules (aggregates 200+ suppliers — returns search URL, not direct data):

eMolecules_search(query="compound_name")
# -> Returns search_url to visit on eMolecules.com

eMolecules_search_smiles(smiles="CANONICAL_SMILES")
# -> Returns URL for exact/similar structure search

Enamine (37B+ make-on-demand compounds — returns URL when API unavailable):

Enamine_search_catalog(query="compound_name")
# -> If API available: returns catalog entries with catalog_id, price
# -> If API unavailable: returns search_url for manual search

Enamine_search_smiles(smiles="CANONICAL_SMILES")
# -> Exact or similarity structure search

Enamine_get_libraries()
# -> Lists available Enamine screening collections

Note: eMolecules and Enamine APIs frequently return search URLs rather than live data. Present these to the user as "search here" links.

Tool Parameter Reference

Common Patterns

Pattern 1: Full Compound Profile

Input: Compound name (e.g., "imatinib")
Flow:
  1. PubChem_get_CID_by_compound_name -> CID + SMILES
  2. ChEMBL_search_molecules -> ChEMBL ID
  3. PubChem_get_compound_properties_by_CID -> physicochemical props
  4. SwissADME_calculate_adme / ADMETAI_predict_* -> ADMET profile
  5. ChEMBL_search_activities(molecule_chembl_id) -> binding data
  6. ChEMBL_get_drug_mechanisms -> MOA (if approved drug)
Output: Complete compound profile with identity, ADMET, and activity data

Pattern 2: Analog Discovery

Input: Reference compound SMILES
Flow:
  1. PubChem_search_compounds_by_similarity(smiles, threshold=85) -> similar CIDs
  2. ChEMBL_search_similar_molecules(query=smiles) -> ChEMBL analogs
  3. For each hit: PubChem_get_compound_properties_by_CID -> properties
  4. SwissADME_check_druglikeness -> filter by drug-likeness
Output: Ranked list of analogs with activity data and drug-likeness scores

Pattern 3: Target-Based Compound Search

Input: Target name (e.g., "EGFR")
Flow:
  1. ChEMBL_search_targets(query="EGFR", organism="Homo sapiens") -> target_chembl_id
  2. ChEMBL_get_target_activities(target_chembl_id) -> all ligands with Ki/IC50
  3. Filter by pchembl_value >= 7 (potent compounds)
  4. For top hits: SwissADME_check_druglikeness -> assess drug-likeness
  5. eMolecules_search(query=compound_name) -> check commercial availability
Output: Prioritized list of potent, drug-like, commercially available compounds

Pattern 4: ADMET Risk Assessment

Input: Novel compound SMILES
Flow:
  1. SwissADME_calculate_adme(operation="calculate_adme", smiles) -> full ADMET
  2. ADMETAI_predict_toxicity(smiles=[smiles]) -> hERG, DILI, mutagenicity
  3. ADMETAI_predict_CYP_interactions(smiles=[smiles]) -> drug-drug interaction risk
  4. ADMETAI_predict_BBB_penetrance(smiles=[smiles]) -> CNS penetration
Output: ADMET risk profile with flagged liabilities

Fallback Chains

Limitations

• BindingDB: REST API frequently times out; ChEMBL is the reliable alternative for binding data
• Enamine API: Returns HTTP 500 often; tool provides search URL as fallback
• eMolecules: No public API; tool generates search URLs only
• ADMET-AI: Requires pip install tooluniverse[ml]; not always available in base install
• SwissTargetPrediction: Web scraping-based; may time out for complex molecules
• SMILES format: ADMET-AI requires a list ["SMILES"]; SwissADME requires a string "SMILES"
• ChEMBL IDs: Always use full format "CHEMBL941", never just "941"