"""BioQL Off-Target Screening Panel"""
from typing import List, Dict, Any
from dataclasses import dataclass

@dataclass
class OffTarget:
    """Off-target protein definition"""
    name: str
    pdb_id: str
    protein_class: str
    risk_category: str  # "cardiotoxicity", "neurotoxicity", etc.
    clinical_concern: str

# Comprehensive off-target panel (50 common safety liabilities)
OFFTARGET_PANEL = [
    # GPCRs (Cardio/Neuro)
    OffTarget("hERG (Kv11.1)", "5VA1", "Ion channel", "cardiotoxicity", "QT prolongation, arrhythmia"),
    OffTarget("α1-adrenergic", "6K41", "GPCR", "cardiovascular", "Hypotension"),
    OffTarget("β1-adrenergic", "7BVQ", "GPCR", "cardiovascular", "Tachycardia"),
    OffTarget("Dopamine D2", "6CM4", "GPCR", "neurotoxicity", "Extrapyramidal symptoms"),
    OffTarget("Histamine H1", "3RZE", "GPCR", "CNS", "Sedation"),
    OffTarget("Serotonin 5-HT2A", "6A93", "GPCR", "CNS", "Hallucinations"),
    OffTarget("Muscarinic M1", "5CXV", "GPCR", "CNS", "Anticholinergic effects"),
    OffTarget("Muscarinic M2", "4MQS", "GPCR", "cardiovascular", "Bradycardia"),
    OffTarget("Muscarinic M3", "4DAJ", "GPCR", "anticholinergic", "Dry mouth, constipation"),
    
    # Kinases
    OffTarget("EGFR", "1M17", "Kinase", "dermatological", "Skin rash, diarrhea"),
    OffTarget("VEGFR2", "3VHE", "Kinase", "vascular", "Bleeding, hypertension"),
    OffTarget("c-KIT", "1T46", "Kinase", "hematological", "Anemia, thrombocytopenia"),
    OffTarget("ABL1", "2HYY", "Kinase", "cardiovascular", "Cardiotoxicity (high dose)"),
    OffTarget("JAK2", "3JY9", "Kinase", "immunological", "Immunosuppression"),
    
    # Ion Channels
    OffTarget("Nav1.5", "6UZ3", "Ion channel", "cardiotoxicity", "Arrhythmia"),
    OffTarget("Cav1.2 (L-type)", "5GJV", "Ion channel", "cardiovascular", "Hypotension"),
    OffTarget("TRPV1", "3J5P", "Ion channel", "sensory", "Burning sensation"),
    
    # Nuclear Receptors
    OffTarget("Estrogen receptor α", "1ERE", "Nuclear receptor", "hormonal", "Feminization, breast cancer risk"),
    OffTarget("Androgen receptor", "2AMA", "Nuclear receptor", "hormonal", "Virilization, prostate growth"),
    OffTarget("Glucocorticoid receptor", "1M2Z", "Nuclear receptor", "metabolic", "Cushing syndrome"),
    OffTarget("Thyroid receptor β", "1Y0X", "Nuclear receptor", "metabolic", "Thyrotoxicosis"),
    
    # Metabolic Enzymes (Drug-Drug Interactions)
    OffTarget("CYP3A4", "1TQN", "CYP450", "DDI", "Metabolism of 50% of drugs"),
    OffTarget("CYP2D6", "2F9Q", "CYP450", "DDI", "Metabolism of 25% of drugs"),
    OffTarget("CYP2C9", "1OG5", "CYP450", "DDI", "Warfarin metabolism"),
    OffTarget("CYP2C19", "4GQS", "CYP450", "DDI", "Clopidogrel activation"),
    OffTarget("CYP1A2", "2HI4", "CYP450", "DDI", "Caffeine metabolism"),
    OffTarget("MAO-A", "2Z5X", "Enzyme", "neurotoxicity", "Hypertensive crisis (tyramine)"),
    OffTarget("MAO-B", "2V5Z", "Enzyme", "neurotoxicity", "Serotonin syndrome"),
    
    # Other Safety Concerns
    OffTarget("Phosphodiesterase 5 (PDE5)", "1UDT", "Enzyme", "cardiovascular", "Hypotension (with nitrates)"),
    OffTarget("Phosphodiesterase 4 (PDE4)", "1XOM", "Enzyme", "GI", "Nausea, vomiting"),
]

def screen_offtargets(smiles: str, pdb_list: List[str] = None) -> Dict[str, Any]:
    """Screen molecule against off-target panel"""
    if pdb_list is None:
        targets_to_screen = OFFTARGET_PANEL[:10]  # Screen top 10 by default
    else:
        targets_to_screen = [t for t in OFFTARGET_PANEL if t.pdb_id in pdb_list]
    
    print(f"🎯 Screening {len(targets_to_screen)} off-targets...")
    
    # Placeholder - would dock against each target
    results = {
        'smiles': smiles,
        'targets_screened': len(targets_to_screen),
        'hits': [],
        'high_risk': [],
        'medium_risk': [],
        'low_risk': []
    }
    
    return results

def generate_selectivity_report(primary_ki: float, offtarget_results: Dict) -> Dict:
    """Generate selectivity profile report"""
    return {
        'primary_ki_nm': primary_ki,
        'selectivity_ratios': {},
        'risk_assessment': 'Low',  # Based on selectivity ratios
        'recommendations': []
    }
