Admet Prediction

Keywords: ADMET, PK, toxicity, drug-likeness, DILI, hERG, bioavailability category: DMPK tags: [admet, pk, toxicity, drug-likeness, safety] version: 1.0.0 author: Drug Discovery Team dependencies: - rdkit - admet-models

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

| Property | Prediction | Model | |----------|------------|-------| | Absorption | Caco-2, HIA, Pgp | ML/QSAR | | Distribution | VDss, PPB, BBB | ML/QSAR | | Metabolism | CYP inhibition, clearance | ML/QSAR | | Excretion | Clearance, half-life | ML/QSAR | | Toxicity | hERG, DILI, Ames, mutagenicity | ML/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.

Property Ranges

Drug-Likeness

| Rule | Pass Criteria | |------|---------------| | Lipinski Ro5 | ≤ 1 violation | | Veber | RotB ≤ 10, PSA ≤ 140 Ų | | Egan | LogP ≤ 5, PSA ≤ 131 Ų | | MDDR | MW ≤ 600, LogP ≤ 5 |

Absorption

| Property | Good | Moderate | Poor | |----------|------|----------|------| | HIA | >80% | 40-80% | <40% | | Caco-2 | >10 | 1-10 | <1 | | F30% | >70% | 30-70% | <30% |

Distribution

| Property | Good | Moderate | Poor | |----------|------|----------|------| | VDss | 0.3-5 L/kg | <0.3 or >5 | Extreme | | PPB | <90% | 90-95% | >95% | | BBB | LogBB > 0.3 | -0.3 to 0.3 | < -0.3 |

Toxicity Alerts

| Alert | Action | |-------|--------| | hERG inhibition | Cardiotoxicity risk | | DILI positive | Hepatotoxicity risk | | Ames positive | Mutagenicity risk | | PAINS | Assay interference | | Structural alerts | Investigate 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

Best Practices

1. Use multiple models: Consensus predictions more reliable 2. Check confidence: Low confidence = experimental verification needed 3. Consider chemistry: Novel structures less reliable 4. Iterative design: Use predictions to guide synthesis 5. Validate early: Confirm key predictions experimentally

Common Pitfalls

| Pitfall | Solution | |---------|----------| | Over-reliance on predictions | Experimental validation required | | Ignoring confidence | Check model applicability domain | | Single model only | Use consensus of multiple models | | Ignoring chemistry | Novel scaffolds = uncertain predictions | | Late-stage testing | Early ADMET screening saves time |

Limitations

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

1. Use multiple models: Consensus predictions more reliable 2. Check confidence: Low confidence = experimental verification needed 3. Consider chemistry: Novel structures less reliable 4. Iterative design: Use predictions to guide synthesis 5. Validate early: Confirm key predictions experimentally