tooluniverse-admet-prediction

# ADMET Prediction & Drug Candidate Profiling

**ADMET reasoning**: a drug fails if it can't be absorbed, distributes to wrong tissues, isn't metabolized safely, or isn't excreted. Evaluate each property independently — good absorption doesn't compensate for liver toxicity. The ADME properties determine whether a compound reaches its target at therapeutic concentrations; toxicity determines whether it's safe to do so. Prioritize experimental data (T2) over computational predictions (T3) — ADMETAI predictions are screening tools, not definitive verdicts. When a FAIL is flagged in any toxicity category (hERG, AMES, DILI), treat it as program-limiting until wet-lab data refutes it.

**LOOK UP DON'T GUESS**: never assume SMILES, CID, or experimental LD50 values — always call PubChem to resolve compound identity before any ADMETAI or PubChemTox call.

Comprehensive pharmacokinetic and toxicity profiling integrating AI-based ADMET predictions, rule-based drug-likeness filters, and experimental benchmarks from curated databases.

## When to Use This Skill

**Triggers**:
- "What are the ADMET properties of [compound]?"
- "Is [drug] likely to cross the blood-brain barrier?"
- "Predict the toxicity of this SMILES: ..."
- "Does [compound] violate Lipinski's rule of five?"
- "Assess the drug-likeness of [molecule]"
- "What are the CYP interactions for [drug]?"
- "Pharmacokinetic profile of [compound]"
- "Is [compound] orally bioavailable?"
- "What is the LD50 / hERG liability of [molecule]?"

**Input**: Drug name (e.g., "ibuprofen") OR SMILES string (e.g., "CC(C)Cc1ccc(cc1)C(C)C(=O)O")

---

## 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.

## KEY PRINCIPLES

1. **Resolve identity first** - Always convert drug name to SMILES before calling ADMETAI tools
2. **ADMETAI tools require `tooluniverse[ml]`** - If import fails, skip to SwissADME/PubChemTox fallbacks
3. **All ADMETAI tools take `smiles: list[str]`** - Always wrap in a list, even for one compound
4. **SwissADME takes `smiles: str`** - Single string, NOT a list (SOAP-style with `operation` param)
5. **PubChemTox tools accept `cid` or `compound_name`** - Use CID when available for reliability
6. **Evidence grading mandatory** - Predictions (T3), experimental data (T2), regulatory (T1)
7. **Scorecard output** - Every analysis must end with a pass/warn/fail scorecard
8. **Explain significance** - State WHY each property matters for drug development

---

## Evidence Grading

| Tier | Label | Source |
|------|-------|--------|
| **T1** | Regulatory/Clinical | FDA labels, ChEMBL max clinical phase |
| **T2** | Experimental | PubChemTox LD50/LC50, in vitro AMES, animal studies |
| **T3** | Computational | ADMETAI predictions, SwissADME calculations |
| **T4** | Annotation | Database cross-references, text-mined |

## Workflow: 5-Phase ADMET Profiling

```
User Query (drug name or SMILES)
|
+-- PHASE 1: Compound Identity Resolution
|   PubChem name->CID->SMILES, or validate input SMILES
|
+-- PHASE 2: Physicochemical & Drug-Likeness
|   ADMETAI physicochemical + SwissADME druglikeness -> Lipinski/Veber
|
+-- PHASE 3: ADME Predictions
|   BBB, bioavailability, CYP interactions, clearance, solubility
|
+-- PHASE 4: Toxicity Assessment
|   ADMETAI tox + PubChemTox experimental + nuclear receptor + stress
|
+-- PHASE 5: Scorecard & Clinical Context
|   ChEMBL max phase, aggregate pass/warn/fail, final recommendation
```

---

### PHASE 1: Compound Identity Resolution

**Goal**: Obtain SMILES, PubChem CID, and basic identifiers for the query compound.

**Steps**:

1. **If input is a drug name**:
   - Call `PubChem_get_CID_by_compound_name(name=<drug_name>)` to get CID
   - Call `PubChem_get_compound_properties_by_CID(cid=<CID>)` to get SMILES and MW
   - Extract `ConnectivitySMILES` from the response (NOT `CanonicalSMILES`)

2. **If input is a SMILES string**:
   - Call `PubChem_get_CID_by_SMILES(smiles=<SMILES>)` to get CID
   - Call `PubChem_get_compound_properties_by_CID(cid=<CID>)` for compound name and MW
   - Use the input SMILES for all subsequent ADMETAI calls

3. **Record**:
   - Compound name, CID, SMILES, molecular formula, molecular weight, IUPAC name
   - If CID lookup fails, proceed with SMILES only (ADMETAI does not need CID)

**Why this matters**: ADMETAI tools require SMILES input. PubChemTox tools work best with CID. Resolving both ensures all downstream tools can be called. PubChem is the authoritative source for SMILES canonicalization.

**Fallback**: If PubChem has no entry, the user must provide SMILES directly. Cannot proceed without SMILES.

---

### PHASE 2: Physicochemical Properties & Drug-Likeness

**Goal**: Evaluate whether the compound has drug-like physicochemical properties.

**Steps**:

1. **ADMETAI physicochemical** (primary):
   ```
   ADMETAI_predict_physicochemical_properties(smiles=["<SMILES>"])
   ```
   Returns: MW, logP, TPSA, HBD, HBA, rotatable bonds

2. **SwissADME drug-likeness** (complementary):
   ```
   SwissADME_check_druglikeness(operation="check_druglikeness", smiles="<SMILES>")
   SwissADME_calculate_adme(operation="calculate_adme", smiles="<SMILES>")
   ```
   Returns: Lipinski, Veber, Ghose, Egan, Muegge rule compliance; PAINS alerts; Brenk alerts

3. **ADMETAI solubility**:
   ```
   ADMETAI_predict_solubility_lipophilicity_hydration(smiles=["<SMILES>"])
   ```
   Returns: Aqueous solubility (LogS), lipophilicity, hydration free energy

**Interpret & Score**:

| Property | Ideal Range | Why It Matters |
|----------|-------------|----------------|
| MW | < 500 Da | Larger molecules have poor membrane permeability (Lipinski) |
| LogP | -0.4 to 5.6 | Too hydrophobic = poor solubility; too hydrophilic = poor permeability |
| HBD | <= 5 | Excess donors reduce membrane crossing (Lipinski) |
| HBA | <= 10 | Excess acceptors reduce membrane crossing (Lipinski) |
| TPSA | < 140 A^2 | High PSA correlates with poor oral absorption |
| Rotatable bonds | <= 10 | Molecular flexibility affects bioavailability (Veber) |
| LogS | > -6 | Below -6 = practically insoluble, formulation challenge |
| PAINS alerts | 0 | Pan-assay interference compounds give false positives in screens |

**Verdict**: PASS if Lipinski <= 1 violation and no PAINS alerts; WARN if 2 violations; FAIL if 3+ violations or PAINS+.

**Fallback**: If ADMETAI import fails (missing `tooluniverse[ml]`), rely on SwissADME alone. SwissADME provides all Lipinski descriptors independently.

---

### PHASE 3: ADME Predictions

**Goal**: Predict absorption, distribution, metabolism, and excretion behavior.

**Steps**:

1. **Blood-brain barrier penetration**:
   ```
   ADMETAI_predict_BBB_penetrance(smiles=["<SMILES>"])
   ```
   - BBB+ = compound can cross; BBB- = cannot
   - Critical for CNS drugs (must cross) and peripherally-acting drugs (should NOT cross to avoid CNS side effects)

2. **Oral bioavailability**:
   ```
   ADMETAI_predict_bioavailability(smiles=["<SMILES>"])
   ```
   - F20% = at least 20% oral bioavailability; F30% = at least 30%
   - Low bioavailability means the drug is extensively metabolized or poorly absorbed
   - F < 20% generally requires non-oral routes (IV, inhaled, topical)

3. **CYP450 interactions**:
   ```
   ADMETAI_predict_CYP_interactions(smiles=["<SMILES>"])
   ```
   - Reports substrate/inhibitor status for CYP1A2, 2C9, 2C19, 2D6, 3A4
   - **Why CYP matters**: ~75% of drugs are metabolized by CYP enzymes. Inhibiting CYP3A4 (which metabolizes ~50% of drugs) causes dangerous drug-drug interactions (DDIs). CYP2D6 polymorphisms affect ~25% of drugs -- poor metabolizers accumulate toxic levels
   - Substrate of CYP2D6 = pharmacogenomic risk (poor/ultra-rapid metabolizers)
   - Inhibitor of CYP3A4 = high DDI risk (co-administered drugs accumulate)

4. **Clearance and distribution**:
   ```
   ADMETAI_predict_clearance_distribution(smiles=["<SMILES>"])
   ```
   - VDss (volume of distribution): low (<0.7 L/kg) = confined to plasma; high (>1 L/kg) = distributed to tissues
   - Clearance: high clearance = short half-life, frequent dosing needed
   - Plasma protein binding (PPB): >95% bound = narrow therapeutic window, DDI risk from displacement

5. **SwissADME pharmacokinetics** (cross-validation):
   - GI absorption (high/low), P-gp substrate status, skin permeation (logKp)

**Key flags**: BBB+ for non-CNS drug (WARN: CNS side effects); BBB- for CNS drug (FAIL: won't reach target); F < 20% (WARN: poor oral bioavailability); CYP3A4 inhibitor (WARN: high DDI); CYP2D6 substrate (WARN: pharmacogenomic variability); PPB > 99% (WARN: narrow window); high clearance + low bioavailability (FAIL).

**Fallback**: If ADMETAI unavailable, SwissADME provides GI absorption, BBB permeation (yes/no), P-gp substrate, and CYP inhibition predictions.

---

### PHASE 4: Toxicity Assessment

**Goal**: Evaluate safety liabilities from both predicted and experimental sources.

**Steps**:

1. **ADMETAI toxicity predictions** [T3]:
   ```
   ADMETAI_predict_toxicity(smiles=["<SMILES>"])
   ```
   Key endpoints:
   - **AMES**: Mutagenicity (bacterial reverse mutation test). Positive = potential carcinogen; regulatory agencies require AMES testing for all new drugs
   - **DILI**: Drug-induced liver injury risk. Leading cause of drug withdrawal (e.g., troglitazone). Positive = hepatotoxicity concern requiring liver function monitoring
   - **hERG**: hERG potassium channel inhibition. Causes QT prolongation and fatal cardiac arrhythmia. hERG+ = cardiotoxicity liability; multiple drugs withdrawn for this (e.g., terfenadine, cisapride)
   - **ClinTox**: Clinical trial toxicity / FDA withdrawal risk. Trained on drugs that failed trials or were withdrawn for toxicity
   - **LD50_Zhu**: Predicted lethal dose (mg/kg, rat oral). Lower = more acutely toxic
   - **Skin_Reaction**: Dermal sensitization potential. Important for topical drugs
   - **Carcinogens_Lagunin**: Carcinogenicity prediction

2. **Nuclear receptor activity** [T3]:
   ```
   ADMETAI_predict_nuclear_receptor_activity(smiles=["<SMILES>"])
   ```
   - AR (androgen receptor), ER (estrogen receptor), AhR, PPAR-gamma activity
   - Positive = potential endocrine disruption; critical for chronic-use drugs and environmental chemicals

3. **Stress response pathways** [T3]:
   ```
   ADMETAI_predict_stress_response(smiles=["<SMILES>"])
   ```
   - p53 activation = DNA damage response (genotoxicity signal)
   - MMP disruption = mitochondrial toxicity
   - ATAD5 = DNA repair stress
   - HSE = heat shock / protein misfolding stress

4. **PubChemTox experimental data** [T2] (call all in parallel):
   ```
   PubChemTox_get_toxicity_values(cid=<CID>)
   PubChemTox_get_ghs_classification(cid=<CID>)
   PubChemTox_get_acute_effects(cid=<CID>)
   PubChemTox_get_carcinogen_classification(cid=<CID>)
   PubChemTox_get_target_organs(cid=<CID>)
   PubChemTox_get_toxicity_summary(cid=<CID>)
   ```
   - Real animal study data (LD50, LC50, NOAEL) anchors computational predictions
   - GHS classification provides internationally harmonized hazard categories
   - Carcinogen classification from IARC (Group 1/2A/2B), NTP, EPA

**Key flags**: AMES positive (FAIL: mutagenic); DILI positive (WARN: hepatotox); hERG positive (FAIL: cardiac, often program-killing); ClinTox positive (WARN); LD50 < 50 mg/kg (FAIL: GHS 1-2); LD50 50-300 mg/kg (WARN: GHS 3); NR-ER/AR active (WARN: endocrine disruption); p53 active (WARN: genotoxicity); IARC Group 1/2A (FAIL: known/probable carcinogen).

**Fallback**: If ADMETAI unavailable, PubChemTox provides experimental toxicity data for known compounds. For novel compounds without PubChem entries, flag as "no experimental toxicity data available -- computational predictions only."

---

### PHASE 5: Scorecard Assembly & Clinical Context

**Goal**: Aggregate all findings into a structured ADMET scorecard with pass/warn/fail verdicts.

**Steps**:

1. **ChEMBL clinical status** [T1] (if drug has ChEMBL ID):
   ```
   ChEMBL_get_molecule(chembl_id="<CHEMBL_ID>")
   ```
   - Max phase: 4 = approved, 3 = Phase III, 2 = Phase II, 1 = Phase I, 0 = preclinical
   - Ro5 violations from ChEMBL (independent validation of Lipinski)
   - First approval year, indication class, black box warning flag

2. **Build the ADMET Scorecard**: produce a table with 13 categories (Physicochemical, Solubility, Absorption, Distribution, Metabolism, Excretion, Tox: Mutagenicity/Hepatotoxicity/Cardiotoxicity/Carcinogenicity/Acute, Endocrine, Clinical Tox), each with PASS/WARN/FAIL verdict and key finding. Include compound identity header and overall verdict. Tag each finding with evidence tier [T1-T3].

3. **Interpretation narrative**: After the scorecard, provide a 3-5 sentence summary:
   - Highlight the most critical findings (any FAILs or WARNs)
   - State whether the compound is suitable for oral administration
   - Note any DDI risks from CYP interactions
   - Flag pharmacogenomic concerns (CYP2D6 substrate)
   - Recommend next steps (e.g., "hERG patch clamp assay recommended to confirm computational prediction")

---

## Completeness Checklist (MANDATORY before reporting)

Before delivering the final scorecard, verify:

- [ ] Compound identity resolved (name, CID, SMILES all present or explicitly noted as unavailable)
- [ ] Physicochemical properties reported with Lipinski verdict
- [ ] At least one source for each ADME property (ADMETAI or SwissADME)
- [ ] All 7 ADMETAI toxicity endpoints reported (or marked N/A with reason)
- [ ] PubChemTox experimental data checked (even if "no data found")
- [ ] Nuclear receptor and stress response checked (or marked N/A)
- [ ] Evidence tier tagged for every finding
- [ ] Scorecard table complete with verdicts for all 13 categories
- [ ] Overall verdict stated
- [ ] Interpretation narrative provided with actionable next steps