Small Molecule Discovery Skill

Systematic small molecule identification, characterization, and sourcing using PubChem, ChEMBL, BindingDB, ADMET-AI, SwissADME, eMolecules, and Enamine. Covers the full pipeline from compound name to structure, activity, ADMET properties, and commercial procurement.

Domain Reasoning

Drug-likeness is not a binary property. Lipinski's Rule of 5 was derived from orally administered, passively absorbed drugs and has many well-known exceptions: natural products, macrocycles, PROTACs, and many approved drugs violate one or more rules. The relevant question is not "does this pass Ro5?" but "does this compound's physicochemical profile match the requirements of the target, the intended route of administration, and the therapeutic context?" Focus on the specific requirements, not rigid rules.

LOOK UP DON'T GUESS

• Compound identity (CID, ChEMBL ID, SMILES): call PubChem_get_CID_by_compound_name and ChEMBL_search_molecules; do not assume IDs from memory.
• ADMET properties: run SwissADME_calculate_adme or ADMETAI_predict_* on the actual SMILES; do not estimate logP, TPSA, or bioavailability.
• Binding affinities against a target: query ChEMBL_search_activities or BindingDB_get_ligands_by_uniprot; never cite IC50 values from memory.
• Commercial availability: check eMolecules_search or Enamine_search_catalog; do not assume availability.

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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

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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

Tool	Purpose	Key Params
PubChem_get_CID_by_compound_name	Name to CID lookup	compound_name
PubChem_get_CID_by_SMILES	SMILES to CID lookup	smiles
PubChem_get_compound_properties_by_CID	MW, formula, SMILES, InChIKey	cid, properties
PubChem_search_compounds_by_similarity	Find structurally similar compounds	smiles, threshold (0-100)
PubChem_search_compounds_by_substructure	Substructure search	smiles
PubChem_get_compound_synonyms_by_CID	All names/synonyms	cid
ChEMBL_search_molecules	Search ChEMBL by name or ID	query
ChEMBL_get_molecule	Full ChEMBL molecule record	chembl_id
ChEMBL_search_similar_molecules	Similarity search in ChEMBL	query (SMILES or ChEMBL ID)
ChEMBL_search_activities	Binding affinities and assay data	molecule_chembl_id, target_chembl_id, pchembl_value__gte
ChEMBL_get_drug_mechanisms	MOA for approved drugs	drug_chembl_id or drug_name
ChEMBL_search_targets	Find targets by name	query, organism
ChEMBL_get_target_activities	All ligands for a target	target_chembl_id
SwissADME_calculate_adme	Physicochemical + ADMET properties	operation="calculate_adme", smiles
SwissADME_check_druglikeness	Lipinski, Veber, Egan rules	operation="check_druglikeness", smiles
ADMETAI_predict_physicochemical_properties	MW, logP, TPSA, HBD/HBA	smiles (list)
ADMETAI_predict_bioavailability	Oral bioavailability prediction	smiles (list)
ADMETAI_predict_BBB_penetrance	Blood-brain barrier permeability	smiles (list)
ADMETAI_predict_toxicity	hERG, DILI, mutagenicity	smiles (list)
ADMETAI_predict_CYP_interactions	CYP450 inhibition/substrate	smiles (list)
SwissTargetPrediction_predict	Predict protein targets for compound	operation="predict", smiles
eMolecules_search	Find commercially available compounds	query (name or keyword)
eMolecules_search_smiles	Structure-based commercial search	smiles
eMolecules_get_vendors	Find vendors for a specific compound	compound_id
Enamine_search_catalog	Search Enamine screening library	query
Enamine_search_smiles	Search Enamine by structure	smiles
Enamine_get_libraries	List Enamine compound libraries	(none required)

---

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:

pChEMBL	IC50 / Ki	Affinity
>= 9	<= 1 nM	Very potent
>= 7	<= 100 nM	Potent
>= 6	<= 1 µM	Moderate
>= 5	<= 10 µM	Weak
< 5	> 10 µM	Inactive

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.

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Tool Parameter Reference

Tool	Required Params	Notes
PubChem_get_CID_by_compound_name	compound_name	Returns list of CIDs; take first or most relevant
PubChem_get_CID_by_SMILES	smiles	Use canonical SMILES
PubChem_get_compound_properties_by_CID	cid, properties	cid as string; properties comma-separated
PubChem_search_compounds_by_similarity	smiles	threshold (int 0-100, default 90)
PubChem_search_compounds_by_substructure	smiles	Returns CIDs matching scaffold
ChEMBL_search_molecules	query	Name, ChEMBL ID, or InChIKey
ChEMBL_get_molecule	chembl_id	Full format: "CHEMBL941" not "941"
ChEMBL_search_similar_molecules	query	SMILES or ChEMBL ID
ChEMBL_search_activities	molecule_chembl_id OR target_chembl_id	Use pchembl_value__gte=6 to filter potent
ChEMBL_get_drug_mechanisms	drug_chembl_id or drug_name	For approved drugs only
ChEMBL_search_targets	query	Add organism="Homo sapiens" to filter human
ChEMBL_get_target_activities	target_chembl_id	Returns all ligands for target
SwissADME_calculate_adme	operation="calculate_adme", smiles	SMILES as string (not list)
SwissADME_check_druglikeness	operation="check_druglikeness", smiles	SMILES as string
ADMETAI_predict_*	smiles	Must be a list: ["SMILES"] not "SMILES"
SwissTargetPrediction_predict	operation="predict", smiles	May time out
eMolecules_search	query	Returns search URL (no live data)
eMolecules_search_smiles	smiles	Canonical SMILES
eMolecules_get_vendors	compound_id	eMolecules internal ID
Enamine_search_catalog	query	Returns URL when API unavailable
Enamine_search_smiles	smiles	search_type: "exact", "similarity", "substructure"
Enamine_get_compound	enamine_id	Enamine-specific catalog ID
BindingDB_get_ligands_by_uniprot	uniprot_id	Frequently unavailable — use ChEMBL as fallback
BindingDB_get_targets_by_compound	smiles	SMILES-based target lookup

---

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

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Fallback Chains

Primary	Fallback	When
BindingDB_get_ligands_by_uniprot	ChEMBL_get_target_activities	BindingDB API unavailable
ADMETAI_predict_*	SwissADME_calculate_adme	ml dependencies not installed
Enamine_search_catalog	Returns URL only	API returns HTTP 500 (common)
SwissTargetPrediction_predict	ChEMBL_search_similar_molecules + known targets	Prediction times out
PubChem_get_CID_by_compound_name	ChEMBL_search_molecules(query=name)	Name not in PubChem

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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"