ahmedmiloudi/BioTechLabAI

1"""2Bioactivity Profiler Module3============================4Analyse de bioactivité et SAR (Structure-Activity Relationships)5 6Fonctionnalités:7- Analyse IC50/Ki/EC508- SAR analysis9- Dose-response curves10- Selectivity profiling11 12Auteur: Drug Discovery Platform13Date: 2026-01-3114"""15 16import pandas as pd17import numpy as np18from typing import Dict, List, Optional19import logging20 21logger = logging.getLogger(__name__)22 23 24class BioactivityProfiler:25    """Profiler pour données de bioactivité"""26    27    def __init__(self):28        logger.info("BioactivityProfiler initialized")29    30    def analyze_potency_distribution(self, bioactivities: List) -> Dict:31        """Analyse distribution des potences"""32        if not bioactivities:33            return {}34        35        values = [b.activity_value for b in bioactivities if b.activity_value > 0]36        37        return {38            'count': len(values),39            'mean_nM': np.mean(values),40            'median_nM': np.median(values),41            'std_nM': np.std(values),42            'min_nM': np.min(values),43            'max_nM': np.max(values),44            'potent_compounds': sum(1 for v in values if v < 100),  # < 100nM45            'moderate_compounds': sum(1 for v in values if 100 <= v < 1000),46            'weak_compounds': sum(1 for v in values if v >= 1000)47        }48    49    def calculate_selectivity(self, target_ic50: float, offtarget_ic50s: List[float]) -> Dict:50        """Calcule sélectivité"""51        selectivities = [off / target_ic50 for off in offtarget_ic50s if off > 0]52        53        return {54            'mean_selectivity': np.mean(selectivities) if selectivities else 0,55            'max_selectivity': np.max(selectivities) if selectivities else 0,56            'selective_ratio': sum(1 for s in selectivities if s > 10) / len(selectivities) if selectivities else 057        }58    59    def identify_sar_trends(self, compounds_df: pd.DataFrame) -> List[Dict]:60        """Identifie trends SAR (simplifié)"""61        trends = []62        63        if 'MW' in compounds_df.columns and 'IC50' in compounds_df.columns:64            corr = compounds_df[['MW', 'IC50']].corr().iloc[0, 1]65            if abs(corr) > 0.5:66                trends.append({67                    'feature': 'Molecular Weight',68                    'correlation': corr,69                    'trend': 'positive' if corr > 0 else 'negative'70                })71        72        return trends73    74    def predict_activity_class(self, ic50_nM: float) -> str:75        """Classe activité"""76        if ic50_nM < 10:77            return 'Very Potent'78        elif ic50_nM < 100:79            return 'Potent'80        elif ic50_nM < 1000:81            return 'Moderate'82        else:83            return 'Weak'84 85if __name__ == "__main__":86    profiler = BioactivityProfiler()87    print("BioactivityProfiler ready")88