Admet Prediction

ADMET Prediction Skill

Predict ADMET properties to prioritize compounds for development.

Quick Start

/admet "CC1=CC=C(C=C1)CNC" --full /pk-prediction --library compounds.sdf --threshold 0.7 /toxicity-screen CHEMBL210 --include hERG,DILI,Ames

What's Included

PropertyPredictionModelAbsorptionCaco-2, HIA, PgpML/QSARDistributionVDss, PPB, BBBML/QSARMetabolismCYP inhibition, clearanceML/QSARExcretionClearance, half-lifeML/QSARToxicityhERG, DILI, Ames, mutagenicityML/QSAR

Output Structure

• # ADMET Profile: CHEMBL210 (Osimertinib)
• ## Summary
• | Property | Value | Status |
• |----------|-------|--------|
• | Drug-likeness | Pass | ✓ |
• | Lipinski Ro5 | 0 violations | ✓ |
• | VEBER | Pass | ✓ |
• | PAINS | 0 alerts | ✓ |
• | Brenk | 0 alerts | ✓ |
• ## Absorption
• | Property | Prediction | Confidence |
• |----------|------------|-------------|
• | HIA | 98% | High |
• | Caco-2 | 15.2 × 10⁻⁶ cm/s | High |
• | Pgp substrate | Yes | Medium |
• | F30% | 65% | Medium |
• ## Distribution
• | Property | Prediction | Confidence |
• |----------|------------|-------------|
• | VDss | 5.2 L/kg | Medium |
• | PPB | 95% | High |
• | BBB | Yes | High |
• | CNS MPO | 5.5 | Good |
• ## Metabolism
• | Property | Prediction | Confidence |
• |----------|------------|-------------|
• | CYP3A4 substrate | Yes | High |
• | CYP3A4 inhibitor | Yes | Medium |
• | CYP2D6 inhibitor | No | High |
• | CYP2C9 inhibitor | No | Medium |
• | Clearance | 8.5 mL/min/kg | Low |
• ## Excretion
• | Property | Prediction | Confidence |
• |----------|------------|-------------|
• | Renal clearance | 10% | Medium |
• | Half-life | 48 hours | High |
• ## Toxicity
• | Property | Prediction | Confidence |
• |----------|------------|-------------|
• | hERG inhibition | No | High |
• | DILI | Concern | Medium |
• | Ames mutagenicity | Negative | High |
• | Carcinogenicity | Negative | Medium |
• | Respiratory toxicity | No | Low |
• ## Recommendations
• **Strengths**:
• Good oral bioavailability (65%)
• Brain penetration (BBB permeable)
• Low hERG risk
• **Concerns**:
• DILI concern - monitor in preclinical studies
• CYP3A4 inhibition - potential DDIs
• **Overall**: Good ADMET profile. Progress to in vivo PK.

Drug-Likeness

RulePass CriteriaLipinski Ro5≤ 1 violationVeberRotB ≤ 10, PSA ≤ 140 ŲEganLogP ≤ 5, PSA ≤ 131 ŲMDDRMW ≤ 600, LogP ≤ 5

Absorption

PropertyGoodModeratePoorHIA>80%40-80%<40%Caco-2>101-10<1F30%>70%30-70%<30%

Distribution

PropertyGoodModeratePoorVDss0.3-5 L/kg<0.3 or >5ExtremePPB<90%90-95%>95%BBBLogBB > 0.3-0.3 to 0.3< -0.3

Toxicity Alerts

AlertActionhERG inhibitionCardiotoxicity riskDILI positiveHepatotoxicity riskAmes positiveMutagenicity riskPAINSAssay interferenceStructural alertsInvestigate further

Running Scripts

# Full ADMET profile python scripts/admet_predict.py --smiles "CC1=CC=C..." --full # Batch prediction python scripts/admet_predict.py --library compounds.sdf --output results.csv # Specific properties python scripts/admet_predict.py --smiles "..." --properties hERG,DILI,CYP # Filter by criteria python scripts/admet_filter.py --library compounds.sdf --rules lipinski,veber

Requirements

pip install rdkit # Optional for advanced models pip install deepchem admet-x

Reference

reference/admet-properties.md - Detailed property reference reference/toxicity-alerts.md - Toxicity alerts reference reference/pk-models.md - PK prediction models

Best Practices

Use multiple models: Consensus predictions more reliable Check confidence: Low confidence = experimental verification needed Consider chemistry: Novel structures less reliable Iterative design: Use predictions to guide synthesis Validate early: Confirm key predictions experimentally

Common Pitfalls

PitfallSolutionOver-reliance on predictionsExperimental validation requiredIgnoring confidenceCheck model applicability domainSingle model onlyUse consensus of multiple modelsIgnoring chemistryNovel scaffolds = uncertain predictionsLate-stage testingEarly ADMET screening saves time

Limitations

Models are approximate: Errors common Novel chemistry: Less reliable for new scaffolds In vitro-in vivo gap: Predictions don't always translate Species differences: Human predictions based on animal data Complex mechanisms: Some toxicity not predicted