tooluniverse-small-molecule-discovery

What this skill does

# 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.

---

**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

| 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_