| name | admet-prediction |
| description | ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction
for drug candidates. Use for assessing drug-likeness, PK properties, and
safety risks early in drug discovery.
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
- 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
| 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
- 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