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SKILL.md
ADME Property Predictor
Overview
Comprehensive pharmacokinetic prediction tool that assesses drug-likeness and ADME properties of small molecules using validated cheminformatics models, molecular descriptors, and structure-property relationships.
Causes: Model extrapolation errors; invalid input structures
Solutions:
Check input structure validity
Cap extreme values at physiologically plausible limits
Flag for manual review if outside typical ranges
Problem: Batch processing extremely slow
Symptoms: "100 compounds taking 30 minutes"
Causes: Single-threaded execution; complex models
Solutions:
Enable parallel processing (--n-workers 4)
Use faster models for initial screening (QSAR vs ML)
Pre-filter with rule-based methods (Lipinski) before detailed ADME
Problem: Inconsistent predictions across runs
Symptoms: "Same compound, different predictions on re-run"
Causes: Random seed issues; stochastic models
Solutions:
Set random seeds for reproducibility
Use deterministic models when consistency critical
Average multiple predictions if stochastic models necessary
Problem: Properties contradict each other
Symptoms: "High LogP (4.5) but predicted very soluble"
Causes: Model inconsistencies; prediction errors
Solutions:
Check input structure (tautomeric form matters for both)
Lipophilic compounds (LogP > 3) typically have poor solubility
Use thermodynamic cycle checks if available
Problem: Cannot process certain file formats
Symptoms: "Error: Unsupported format" for SDF or MOL files
Causes: Format limitations; parser issues
Solutions:
Convert to SMILES using chemical-structure-converter
Check file encoding (UTF-8 vs Latin-1)
Verify structure validity with external tools
References
Available in references/ directory:
lipinski_rules.md - Detailed explanation of Rule of 5 and variants
qsar_models.md - Technical documentation of predictive models
adme_databases.md - Experimental ADME data sources for validation
property_ranges.md - Acceptable ranges for marketed drugs by class
model_validation.md - Validation statistics and applicability domains
cheminformatics_basics.md - Introduction to molecular descriptors
Scripts
Located in scripts/ directory:
main.py - CLI interface for ADME prediction
adme_predictor.py - Core prediction engine
absorption.py - Absorption property models
distribution.py - Distribution property models
metabolism.py - Metabolism prediction models
excretion.py - Excretion and clearance models
druglikeness.py - QED, MPO, and other scoring functions
batch_processor.py - Library screening and parallel processing
validator.py - Input validation and applicability domain checking
Performance and Resources
Prediction Speed:
Task
Time
Hardware
Single compound
0.5-2 sec
CPU
100 compounds
30-60 sec
CPU
1000 compounds
5-10 min
CPU
1000 compounds
2-3 min
4-core parallel
10,000 compounds
30-60 min
4-core parallel
System Requirements:
RAM: 4 GB minimum; 8 GB for large libraries (>10K compounds)
Storage: 100 MB for models and dependencies
CPU: Multi-core recommended for batch processing
No GPU required: All models CPU-based
Optimization Tips:
Process libraries in batches of 5000-10000
Use rule-based filters (Lipinski) before expensive ML predictions
Cache results to avoid re-prediction
Parallel processing scales nearly linearly up to 8 cores
Limitations
Small Molecules Only: Models trained on drugs with MW 100-800 Da; unreliable for larger compounds
pH 7.4 Assumption: Most models predict properties at physiological pH
Human-Specific: Predictions for human PK; animal models may differ
Healthy Subject Assumption: Does not account for disease states, drug interactions
Single Compound: Does not predict formulation effects, salt form impact
Static Models: Do not account for induction, inhibition, or time-dependent changes
Training Set Bias: Underperforms for novel scaffolds not in training data
Qualitative Only: For Go/No-Go decisions; not for precise quantitative predictions
No Toxicity: ADME only; use separate tools for safety assessment
Model Accuracy (Typical):
LogP: R² = 0.85-0.95 (very good)
Solubility: R² = 0.65-0.80 (moderate)
HIA: Accuracy = 75-85% (good)
BBB: Accuracy = 70-80% (moderate)
Metabolic stability: R² = 0.60-0.75 (moderate)
T1/2: R² = 0.50-0.65 (challenging)
Version History
v1.0.0 (Current): Initial release with 20+ ADME endpoints, QED scoring, batch processing
Planned: Integration with PK simulation, population variability modeling, formulation effects
⚠️ CRITICAL DISCLAIMER: These predictions are computational estimates for prioritization and guidance only. They do NOT replace experimental ADME studies required for regulatory submissions or clinical decision-making. Always validate predictions with appropriate in vitro and in vivo assays before advancing compounds.
