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