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")

Before You Run

ADMETAI tools run a local model, so they need the ml extra:

bash
uv pip install 'tooluniverse[ml]'

Without it the tools still appear in tu list (the config loads) but fail at call time with ADMETModel requires 'admet-ai' package. Run tooluniverse-doctor to confirm which optional groups are installed.

Expected console noise — not errors. The first ADMETAI call loads PyTorch and prints warnings such as missing-GPU / Trainer messages from PyTorch Lightning, and TypedStorage is deprecated from PyTorch. These are emitted by the underlying libraries during normal CPU inference. Predictions are unaffected — do not report them to the user as failures and do not retry the call because of them. Only treat output as a failure if the tool returns an error field or no predictions.

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)

1. 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

1. 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

1. 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

1. 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)

1. 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)

1. 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)

1. 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

1. 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

1. 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

1. 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

1. 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

1. 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].

1. 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