undefined

**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

PubChem_search_compounds_by_similarity(
    smiles="CANONICAL_SMILES",
    threshold=85   # Tanimoto threshold 0-100; 85 = highly similar
)

**Substructure search** (find compounds containing a scaffold):

undefined

ChEMBL_search_activities(
    molecule_chembl_id="CHEMBL941",
    pchembl_value__gte=6,   # pIC50/Ki >= 6 = IC50/Ki <= 1 µM
    limit=50
)

**Get all ligands for a target**:

**BindingDB** (when available — often times out):

**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 |

SwissADME_calculate_adme(
    operation="calculate_adme",
    smiles="CANONICAL_SMILES"
)

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

**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)

SwissTargetPrediction_predict(
    operation="predict",
    smiles="CANONICAL_SMILES"
)

undefined

eMolecules_search(query="compound_name")

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

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

---

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

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

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

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