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 | 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 ```markdown # 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 ```bash # 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 ```bash pip install rdkit # Optional for advanced models pip install deepchem admet-x ``` ## Reference - [reference/admet-properties.md](reference/admet-properties.md) - Detailed property reference - [reference/toxicity-alerts.md](reference/toxicity-alerts.md) - Toxicity alerts reference - [reference/pk-models.md](reference/pk-models.md) - PK prediction models ## 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 - **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