adme-property-predictor

# 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. **Key Capabilities:** - **Multi-Property Prediction**: Absorption, Distribution, Metabolism, Excretion - **Drug-Likeness Scoring**: Lipinski's Rule of 5, Veber rules, QED score - **Batch Processing**: Analyze compound libraries efficiently - **Structure-Based Insights**: Identify liability hotspots and optimization opportunities - **Comparative Analysis**: Rank candidates by predicted PK profile ## When to Use **✅ Use this skill when:** - Screening compound libraries for drug-like properties in early discovery - Prioritizing lead compounds for advancement based on predicted PK - Identifying ADME liabilities requiring structural optimization - Comparing analogs to select candidates with optimal ADME profiles - Filtering virtual screening hits before synthesis - Generating ADME data for regulatory pre-submission packages - Teaching pharmacokinetics and drug design principles **❌ Do NOT use when:** - Exact PK parameters needed for dosing → Use experimental PK studies - Biologics (antibodies, proteins) → Use `antibody-pk-predictor` - Natural products with complex structures → Models trained on synthetic small molecules - Prodrugs requiring metabolic activation → Use `prodrug-activation-predictor` - Prediction for clinical dosing decisions → **CRITICAL**: Experimental validation required - Assessing toxicity or safety → Use `toxicity-structure-alert` or `admetox-predictor` **Related Skills:** - **上游**: `chemical-structure-converter` (structure preparation), `lipinski-rule-filter` (rule-based filtering) - **下游**: `drug-candidate-evaluator` (integrated scoring), `molecular-dynamics-sim` (detailed binding) ## Integration with Other Skills **Upstream Skills:** - `chemical-structure-converter`: Convert between SMILES, InChI, MOL formats - `lipinski-rule-filter`: Initial rule-based drug-likeness screening - `chemical-structure-converter`: Generate 3D conformers for structure-based predictions - `smiles-de-salter`: Remove salt counterions before analysis **Downstream Skills:** - `drug-candidate-evaluator`: Multi-parameter optimization including ADME - `toxicity-structure-alert`: Assess safety alongside ADME - `target-novelty-scorer`: Evaluate target uniqueness for selected candidates - `biotech-pitch-deck-narrative`: Create investor materials with PK data **Complete Workflow:** ``` Chemical Structure Converter (prepare structures) → Lipinski Rule Filter (initial filtering) → ADME Property Predictor (this skill, detailed PK) → Drug Candidate Evaluator (integrated scoring) → Toxicity Structure Alert (safety check) ``` ## Core Capabilities ### 1. Absorption (A) Prediction Predict intestinal absorption, solubility, and permeability: ```python from scripts.adme_predictor import ADMEPredictor predictor = ADMEPredictor() # Predict absorption properties absorption = predictor.predict_absorption( smiles="CC(=O)Oc1ccccc1C(=O)O", # Aspirin properties=["all"] # or specific: ["hia", "caco2", "solubility"] ) print(absorption.summary()) ``` **Predicted Properties:** | Property | Model | Units | Interpretation | |----------|-------|-------|----------------| | **HIA** | ML + physicochemical | % | Human intestinal absorption; >80% good | | **Caco-2** | QSPR | 10⁻⁶ cm/s | Permeability; >70 high, <25 low | | **Solubility** | QSPR | mg/mL | Aqueous solubility; >0.1 mg/mL acceptable | | **LogS** | QSPR | unitless | Intrinsic solubility; >-4 acceptable | | **Lipinski Pass** | Rule-based | boolean | Passes all 5 rules | | **Veber Pass** | Rule-based | boolean | PSA <140, rotatable bonds <10 | **Best Practices:** - ✅ Consider HIA and solubility together (high HIA but low solubility = dissolution-limited) - ✅ Caco-2 good for oral absorption prediction; poor for BBB penetration - ✅ Use both rule-based (Lipinski) and ML-based predictions for consensus - ✅ Check solubility at physiological pH (not just intrinsic) **Common Issues and Solutions:** **Issue: Lipinski pass but poor solubility** - Symptom: "Passes Rule of 5 but LogS = -5" - Solution: Lipinski checks MW and LogP, not solubility directly; use explicit solubility prediction **Issue: Caco-2 predicts high absorption but HIA low** - Symptom: "Caco-2 = 85 (high) but HIA = 60%" - Solution: Models have different training sets; Caco-2 is in vitro, HIA in vivo; HIA generally more reliable ### 2. Distribution (D) Prediction Predict tissue distribution, protein binding, and brain penetration: ```python # Predict distribution properties distribution = predictor.predict_distribution( smiles="CC(=O)Oc1ccccc1C(=O)O", properties=["vd", "ppb", "bbb"] ) # Access specific predictions vd = distribution.volume_of_distribution bbb = distribution.blood_brain_barrier ppb = distribution.plasma_protein_binding ``` **Predicted Properties:** | Property | Model | Units | Interpretation | |----------|-------|-------|----------------| | **Vd** | QSPR | L/kg | Volume of distribution; 0.1-10 typical | | **PPB** | ML | % | Plasma protein binding; >90% high, <50% low | | **BBB** | LogBB | unitless | Brain penetration; >0.3 penetrant | | **fu** | Calculated | fraction | Free (unbound) fraction; 1 - PPB/100 | **Best Practices:** - ✅ High PPB (>90%) may require higher doses but longer half-life - ✅ Low Vd (<0.3) = mainly in plasma; high Vd (>3) = extensive tissue distribution - ✅ BBB penetration critical for CNS drugs; avoid for peripherally-acting drugs - ✅ fu (free fraction) drives pharmacological activity, not total concentration **Common Issues and Solutions:** **Issue: BBB predictions unreliable for certain chemotypes** - Symptom: "BBB model gives conflicting predictions for peptides" - Solution: Models trained on small molecules; use specialized BBB predictors for peptides, macrocycles **Issue: PPB overestimated for acidic drugs** - Symptom: "PPB predicted 95% but experimental is 70%" - Solution: Some models biased toward neutral/basic compounds; check model training set overlap ### 3. Metabolism (M) Prediction Predict metabolic stability, CYP interactions, and liability sites: ```python # Predict metabolism properties metabolism = predictor.predict_metabolism( smiles="CC(=O)Oc1ccccc1C(=O)O", include_site_prediction=True ) # Check CYP interactions cyp_profile = metabolism.cyp_profile stability = metabolism.metabolic_stability ``` **Predicted Properties:** | Property | Model | Output | Interpretation | |----------|-------|--------|----------------| | **CYP Inhibition** | ML | IC50 or class | Potential DDI; <1 μM high risk | | **CYP Substrate** | Classification | Boolean/Probability | Metabolized by specific CYP | | **Stability** | ML | T1/2 or class | Microsomal/ hepatocyte stability | | **Liability Sites** | Reactivity models | Atom indices | Soft spots for metabolism | | **MAO Substrate** | Classification | Boolean | Monoamine oxidase substrate | **Best Practices:** - ✅ Screen for CYP3A4 inhibition early (most common DDI) - ✅ Check if compound is CYP substrate (for polymorphism concerns) - ✅ Identify metabolic hotspots for structural blocking - ✅ Consider species differences (human vs rodent metabolism) **Common Issues and Solutions:** **Issue: False negatives for time-dependent inhibition (TDI)** - Symptom: "No CYP inhibition predicted but TDI observed experimentally" - Solution: Standard models predict reversible inhibition; use specialized TDI predictors **Issue: Metabolic site prediction shows multiple hotspots** - Symptom: "5 different atoms flagged as metabolic liabilities" - Solution: Prioritize by reactivity score; consider blocking highest-risk site first ### 4. Excretion (E) Prediction Predict clearance routes and elimination kinetics: ```python # Predict excretion properties excretion = predictor.predict_excretion( smiles="CC(=O)Oc1ccccc1C(=O)O", properties=["clearance", "half_life", "route"] ) # Access predictions clearance = excretion.clearance_ml_min_kg t12 = excretion.half_life_hours route = excretion.primary_route ``` **Predicted Properties:** | Property | Model | Units | Interpretation | |----------|-------|-------|----------------| | **CL** | QSPR | mL/min/kg | Clearance; <5 low, 5-15 moderate, >15 high | | **T1/2** | QSPR | hours | Half-life; 2-8h typical for oral drugs | | **Route** | Classification | renal/biliary/mixed | Primary excretion pathway | | **LogD** | QSPR | unitless | Distribution coefficient; affects clearance | **Best Practices:** - ✅ Half-life determines dosing frequency (T1/2 × 5 = time to steady state) - ✅ Renal clearance predictable for polar compounds; hepatic less predictable - ✅ High clearance (>15) may require high doses or prodrug approach - ✅ Very long T1/2 (>24h) good for adherence but risk accumulation **Common Issues and Solutions:** **Issue: Clearance predictions highly variable** - Symptom: "Same compound, different models give CL = 5 vs 20 mL/min/kg" - Solution: Allometry-based methods unreliable for novel scaffolds; use average of multiple models **Issue: Route prediction contradicts structure** - Symptom: "Highly polar compound predicted biliary, expected renal" - Solution: Check LogP/LogD; polar compounds (<0) usually renal; neutral/lipophilic (>1) usually hepatic ### 5. Integrated Drug-Likeness Scoring Overall assessment combining all ADME properties: ```python # Generate comprehensive drug-likeness score druglikeness = predictor.calculate_druglikeness( smiles="CC(=O)Oc1ccccc1C(=O)O", methods=["qed", "muegge", "golden_triangle"] ) # Multi-parameter optimization mpo_score = predictor.mpo_score( smiles="CC(=O)Oc1ccccc1C(=O)O", target_profile={"hia": >80, "bbb": <0.3, "t12": "2-8h"} ) ``` **Scoring Methods:** | Method | Description | Range | Good Score | |--------|-------------|-------|------------| | **QED** | Quantitative Estimation of Drug-likeness | 0-1 | >0.6 | | **Muegge** | Bioavailability score | 0-6 | >4 | | **MPO** | Multi-Parameter Optimization | 0-10 | >6 | **Best Practices:** - ✅ Use QED as quick overall metric; MPO for property-weighted scoring - ✅ Don't rely solely on drug-likeness; efficacy and safety equally important - ✅ Compare to marketed drugs in same class for context - ✅ Track drug-likeness trends during optimization (should improve) **Common Issues and Solutions:** **Issue: Drug-likeness score conflicts with project needs** - Symptom: "CNS drug has low QED (0.5) because high LogP needed for BBB" - Solution: Drug-likeness rules biased toward oral drugs; use category-specific models (CNS, oncology, etc.) ### 6. Batch Processing and Library Screening Analyze compound libraries efficiently: ```python # Batch process library results = predictor.batch_predict( input_file="library.smi", # SMILES file properties=["all"], output_format="csv", n_workers=4 # Parallel processing ) # Filter by criteria filtered = results.filter( lipinski_pass=True, hia__gt=80, t12__between=(2, 8) ) # Rank by multi-parameter score ranked = results.rank(by="mpo_score", ascending=False) ``` **Best Practices:** - ✅ Process in batches of 1000-10000 for memory efficiency - ✅ Save intermediate results (crash recovery) - ✅ Apply filters sequentially (Lipinski first, then detailed ADME) - ✅ Check property distributions to identify outliers **Common Issues and Solutions:** **Issue: Batch processing runs out of memory** - Symptom: "Killed: Out of memory" with 50K compounds - Solution: Process in chunks; use generators instead of loading all into RAM **Issue: Some compounds fail prediction** - Symptom: "30% of library returns NaN" - Solution: Check for invalid SMILES, unusual atoms, or molecules outside training set domain ## Complete Workflow Example **From SMILES to prioritized candidates:** ```bash # Step 1: Predict ADME for single compound python scripts/main.py \ --smiles "CC(=O)Oc1ccccc1C(=O)O" \ --properties all \ --output aspirin_adme.json # Step 2: Batch process compound library python scripts/main.py \ --input library.smi \ --properties absorption,distribution \ --format csv \ --output library_adme.csv # Step 3: Filter and rank python scripts/main.py \ --input library_adme.csv \ --filter "lipinski_pass=True,hia>80" \ --rank-by qed \ --top-n 100 \ --output top_candidates.csv ``` **Python API Usage:** ```python from scripts.adme_predictor import ADMEPredictor from scripts.batch_processor import BatchProcessor # Initialize predictor = ADMEPredictor() batch = BatchProcessor() # Single compound analysis aspirin = predictor.predict_all("CC(=O)Oc1ccccc1C(=O)O") print(f"HIA: {aspirin.absorption.hia}%") print(f"Half-life: {aspirin.excretion.t12} hours") # Batch screening results = batch.process( input_file="library.smi", predictor=predictor, properties=["absorption", "distribution"], n_workers=4 ) # Filter good candidates good_candidates = results[ (results.lipinski_pass == True) & (results.hia > 80) & (results.bbb < 0.3) & (results.t12.between(2, 8)) ] ``` **Expected Output Files:** ``` output/ ├── aspirin_adme.json # Single compound detailed results ├── library_adme.csv # Batch screening results ├── top_candidates.csv # Filtered and ranked candidates ``` ## Quality Checklist **Pre-Prediction Checks:** - [ ] SMILES string is valid and canonical - [ ] Salt forms removed (if analyzing parent compound) - [ ] Tautomeric state appropriate for physiological pH - [ ] Stereochemistry specified (if relevant for activity) **During Prediction:** - [ ] Compound within model applicability domain (check similarity to training set) - [ ] No unusual atoms or functional groups (models trained on typical drug-like space) - [ ] MW in range 100-800 Da (outside range predictions less reliable) - [ ] Predictions complete (no missing values for critical properties) **Post-Prediction Verification:** - [ ] Drug-likeness scores in reasonable range (sanity check) - [ ] Individual properties internally consistent (e.g., high LogP predicts low solubility) - [ ] **CRITICAL**: Comparison to experimental data if available (validate model for chemotype) - [ ] Rankings align with medicinal chemistry intuition **Before Making Decisions:** - [ ] **CRITICAL**: Predictions are NOT experimental data; use for prioritization only - [ ] Multiple orthogonal models give consistent results - [ ] Structural alerts checked (toxicity, reactivity) - [ ] Top candidates selected for experimental validation - [ ] Documentation of model versions and confidence intervals **For Regulatory Submissions:** - [ ] Model validation documented (training set, test set performance) - [ ] Applicability domain clearly defined - [ ] Prediction uncertainty quantified - [ ] Experimental confirmation for key predictions ## Common Pitfalls **Over-Reliance Issues:** - ❌ **Treating predictions as experimental facts** → Poor decision making - ✅ Use predictions for prioritization; experimental validation required for lead optimization - ❌ **Single model dependency** → Miss model-specific biases - ✅ Compare multiple models; consensus predictions more reliable - ❌ **Ignoring prediction confidence** → False sense of certainty - ✅ Check confidence intervals; low confidence predictions need higher scrutiny **Input Issues:** - ❌ **Invalid or non-canonical SMILES** → Wrong compound analyzed - ✅ Validate SMILES before prediction; use canonical forms - ❌ **Analyzing salt forms** → Properties skewed by counterion - ✅ Remove salts using `smiles-de-salter`; analyze free base/acid - ❌ **Ignoring stereochemistry** → Inaccurate predictions for chiral drugs - ✅ Specify stereochemistry explicitly; use 3D descriptors if available **Interpretation Issues:** - ❌ **Focusing on single property** → Miss overall profile - ✅ Consider all ADME properties; use integrated scores like QED or MPO - ❌ **Rigid cutoff application** → Discard good candidates - ✅ Use cutoffs as guidelines; consider project-specific needs - ❌ **Ignoring property correlations** → Unrealistic optimization - ✅ Recognize trade-offs (e.g., increasing LogP improves BBB but reduces solubility) **Domain Issues:** - ❌ **Applying to biologics** → Completely inappropriate - ✅ These models for small molecules only; use specialized tools for biologics - ❌ **Extrapolating beyond training set** → Unreliable predictions - ✅ Check applicability domain; novel scaffolds need experimental validation **Workflow Issues:** - ❌ **No experimental validation** → Continue with false leads - ✅ Always validate top predictions experimentally - ❌ **Not documenting model versions** → Irreproducible results - ✅ Record software version, model versions, prediction dates ## Troubleshooting **Problem: All predictions show "out of domain" warning** - Symptoms: "Compound outside training set" for entire library - Causes: Library contains unusual chemotypes (peptidomimetics, macrocycles, etc.) - Solutions: - Use specialized models for non-traditional chemotypes - Check if input format correct (SMILES vs InChI) - Verify no strange atoms (metals, silicon, etc.) **Problem: Extreme predictions (negative solubility, >100% absorption)** - Symptoms: "LogS = -15" or "HIA = 150%" - 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.** ## Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `--smiles` | str | Required | SMILES string of the molecule | | `--properties` | str | ["all"] | Specific properties to calculate | | `--format` | str | "json" | Output format | | `--input` | str | Required | Input CSV file with SMILES column | | `--output` | str | Required | Output file for results |