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") ## 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`) 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