""" TCL-Quantum H-bond Integration System This module integrates Thought-Compression Language (TCL) with the quantum hydrogen bond protein folding engine to enable superhuman hypothesis generation. Key innovation: TCL compresses complex biological causality into symbolic representations, while quantum H-bond analysis reveals hidden molecular mechanisms. """ import time from typing import Dict, List, Any, Optional, Tuple from dataclasses import dataclass, field from ..thought_compression.tcl_engine import ThoughtCompressionEngine from ..thought_compression.tcl_types import TCLExecutionContext, CognitiveMetrics from ..thought_compression.tcl_symbols import TCLSymbol, SymbolType from ..multiversal.protein_folding_engine import ( ProteinFoldingEngine, ProteinStructure, FoldingParameters ) from .biological_database import ( BiologicalKnowledgeBase, CancerPathway, Drug, Protein, MolecularInteraction, InteractionType, PathwayType ) @dataclass class QuantumProteinAnalysis: """Results from quantum H-bond analysis of a protein""" protein: Protein structure: Optional[ProteinStructure] = None quantum_hbond_energy: float = 0.0 classical_hbond_energy: float = 0.0 quantum_advantage: float = 0.0 coherence_strength: float = 0.0 topological_protection: float = 0.0 collective_effects: float = 0.0 # TCL compression results compressed_symbols: List[str] = field(default_factory=list) causality_depth: int = 0 def to_dict(self) -> Dict: return { "protein": self.protein.gene_name, "quantum_hbond_energy": self.quantum_hbond_energy, "classical_hbond_energy": self.classical_hbond_energy, "quantum_advantage": self.quantum_advantage, "coherence_strength": self.coherence_strength, "topological_protection": self.topological_protection, "collective_effects": self.collective_effects, "compressed_symbols": self.compressed_symbols, "causality_depth": self.causality_depth } @dataclass class TCLCausalChain: """Compressed causal chain in TCL notation""" chain_id: str symbols: List[TCLSymbol] causal_steps: List[str] # Human-readable causal steps tcl_expression: str # Compressed TCL expression quantum_enhancement: float # How much quantum analysis improves this chain biological_validity: float # How biologically plausible (0-1) novelty_score: float # How novel is this hypothesis (0-1) def to_dict(self) -> Dict: return { "chain_id": self.chain_id, "symbols": [s.name for s in self.symbols], "causal_steps": self.causal_steps, "tcl_expression": self.tcl_expression, "quantum_enhancement": self.quantum_enhancement, "biological_validity": self.biological_validity, "novelty_score": self.novelty_score } class TCLQuantumIntegrator: """ Integrates TCL with quantum H-bond analysis for cancer hypothesis generation This system: 1. Compresses biological knowledge into TCL symbols 2. Uses quantum H-bond analysis to find hidden molecular mechanisms 3. Generates causal chains from cancer to cure 4. Scores hypotheses by biological validity and novelty """ def __init__(self, bio_kb: BiologicalKnowledgeBase): self.bio_kb = bio_kb # Initialize TCL engine self.tcl_engine = ThoughtCompressionEngine(enable_quantum_mode=True) self.session_id = self.tcl_engine.create_session("cancer_researcher", cognitive_level=0.8) # Initialize quantum H-bond engine self.quantum_engine = ProteinFoldingEngine( artifacts_dir="./protein_folding_analysis", params=FoldingParameters() ) # Store analysis results self.quantum_analyses: Dict[str, QuantumProteinAnalysis] = {} self.causal_chains: List[TCLCausalChain] = [] # Load biological knowledge into TCL self._load_biological_knowledge_into_tcl() def _load_biological_knowledge_into_tcl(self): """Compress biological knowledge into TCL symbols""" context = self.tcl_engine.sessions[self.session_id] # Create primitive symbols for cancer biology primitives = [ # Cancer concepts TCLSymbol("Κ", "cancer", SymbolType.CONCEPT, "Cancer disease state", {"proliferation": 0.9, "metastasis": 0.7}, [], 0.95, 1.0), TCLSymbol("Ω", "cure", SymbolType.CONCEPT, "Therapeutic cure", {"remission": 0.95, "survival": 0.9}, [], 0.98, 1.0), TCLSymbol("Δ", "mutation", SymbolType.CONCEPT, "Genetic mutation", {"dna_damage": 0.8, "alteration": 0.75}, [], 0.85, 0.9), # Molecular mechanisms TCLSymbol("Ψ", "protein", SymbolType.CONCEPT, "Protein molecular entity", {"structure": 0.9, "function": 0.95}, [], 0.9, 1.0), TCLSymbol("Φ", "phosphorylation", SymbolType.PRIMITIVE, "Phosphate group transfer", {"activation": 0.85, "regulation": 0.9}, [], 0.8, 0.85), TCLSymbol("Θ", "hydrogen_bond", SymbolType.PRIMITIVE, "Quantum hydrogen bond", {"coherence": 0.9, "binding": 0.95}, [], 0.92, 1.0), TCLSymbol("Λ", "quantum_coherence", SymbolType.PRIMITIVE, "Quantum coherent state", {"delocalization": 0.95, "entanglement": 0.9}, [], 0.97, 1.0), # Pathway symbols TCLSymbol("Π", "proliferation", SymbolType.CONCEPT, "Cell proliferation pathway", {"growth": 0.9, "division": 0.85}, [], 0.88, 0.9), TCLSymbol("Α", "apoptosis", SymbolType.CONCEPT, "Programmed cell death", {"death": 0.95, "elimination": 0.9}, [], 0.92, 0.95), TCLSymbol("Ξ", "angiogenesis", SymbolType.CONCEPT, "Blood vessel formation", {"vascularization": 0.9, "tumor_growth": 0.85}, [], 0.87, 0.9), # Drug action symbols TCLSymbol("Δρ", "inhibition", SymbolType.CAUSALITY, "Molecular inhibition", {"block": 0.9, "suppress": 0.85}, [], 0.85, 0.9), TCLSymbol("Σα", "activation", SymbolType.CAUSALITY, "Molecular activation", {"enhance": 0.9, "stimulate": 0.85}, [], 0.85, 0.9), TCLSymbol("Ξη", "target", SymbolType.CAUSALITY, "Molecular targeting", {"binding": 0.95, "specificity": 0.9}, [], 0.9, 0.95), ] for primitive in primitives: context.symbols.add_symbol(primitive) # Add causality relationships causality_map = context.causality # Use symbol IDs for causal links cancer_id = next((s.id for s in primitives if s.name == "Κ"), "") proliferation_id = next((s.id for s in primitives if s.name == "Π"), "") mutation_id = next((s.id for s in primitives if s.name == "Δ"), "") cure_id = next((s.id for s in primitives if s.name == "Ω"), "") apoptosis_id = next((s.id for s in primitives if s.name == "Α"), "") inhibition_id = next((s.id for s in primitives if s.name == "Δρ"), "") activation_id = next((s.id for s in primitives if s.name == "Σα"), "") quantum_id = next((s.id for s in primitives if s.name == "Λ"), "") hbond_id = next((s.id for s in primitives if s.name == "Θ"), "") protein_id = next((s.id for s in primitives if s.name == "Ψ"), "") if cancer_id and proliferation_id: causality_map.add_causal_link(cancer_id, proliferation_id, 0.95) # Cancer causes proliferation if mutation_id and cancer_id: causality_map.add_causal_link(mutation_id, cancer_id, 0.85) # Mutations cause cancer if proliferation_id and cancer_id: causality_map.add_causal_link(proliferation_id, cancer_id, 0.90) # Proliferation causes cancer if apoptosis_id and cure_id: causality_map.add_causal_link(apoptosis_id, cure_id, 0.75) # Apoptosis leads to cure if inhibition_id and proliferation_id: causality_map.add_causal_link(inhibition_id, proliferation_id, 0.85) # Inhibition suppresses proliferation if activation_id and apoptosis_id: causality_map.add_causal_link(activation_id, apoptosis_id, 0.90) # Activation induces apoptosis if quantum_id and hbond_id: causality_map.add_causal_link(quantum_id, hbond_id, 0.92) # Quantum coherence enhances H-bonds if hbond_id and protein_id: causality_map.add_causal_link(hbond_id, protein_id, 0.95) # H-bonds stabilize proteins print(f"✅ Loaded {len(primitives)} TCL symbols from biological knowledge") print(f"✅ Established causal relationships between cancer biology concepts") def analyze_protein_quantum_properties(self, protein: Protein, sequence: Optional[str] = None) -> QuantumProteinAnalysis: """ Analyze a protein using quantum H-bond force law This uses the REAL quantum H-bond analysis from protein_folding_engine """ # Use default sequence if not provided (simulated) if sequence is None: # Create a representative sequence for analysis # In real system, this would fetch from UniProt sequence = "ACDEFGHIKLMNPQRSTVWY" * (len(protein.gene_name) + 2) # Create initial structure structure = self.quantum_engine.initialize_extended_chain(sequence, seed=42) # Analyze quantum H-bond properties energy_result = self.quantum_engine.energy(structure, return_breakdown=True) breakdown = energy_result["energy_breakdown"] quantum_stats = energy_result["quantum_hbond_stats"] # Extract quantum properties quantum_hbond = breakdown.get("hydrogen_bond_quantum_coherence", 0.0) classical_hbond = breakdown.get("hydrogen_bond_classical", 0.0) quantum_advantage = classical_hbond - quantum_hbond # More negative is better analysis = QuantumProteinAnalysis( protein=protein, structure=structure, quantum_hbond_energy=quantum_hbond, classical_hbond_energy=classical_hbond, quantum_advantage=quantum_advantage, coherence_strength=quantum_stats.get("avg_coherence_strength", 0.0), topological_protection=quantum_stats.get("avg_topological_protection", 0.0), collective_effects=quantum_stats.get("avg_collective_effect", 0.0) ) # Compress protein concept into TCL compression_result = self.tcl_engine.compress_concept( self.session_id, f"{protein.gene_name} protein: {protein.function}" ) analysis.compressed_symbols = compression_result["compressed_symbols"] # Get causality depth causality_result = self.tcl_engine.generate_causal_chain( self.session_id, protein.gene_name, depth=5 ) analysis.causality_depth = causality_result.get("chain_complexity", 0) # Store analysis self.quantum_analyses[protein.uniprot_id] = analysis return analysis def generate_cancer_to_cure_causal_chain(self, target_protein: Protein, pathway: CancerPathway, drug: Optional[Drug] = None) -> TCLCausalChain: """ Generate a causal chain from cancer to cure using TCL compression This is the CORE innovation: compressing complex biology into symbolic causal chains that reveal novel therapeutic strategies """ chain_id = f"chain_{target_protein.gene_name}_{int(time.time())}" # Analyze target protein quantum properties quantum_analysis = self.analyze_protein_quantum_properties(target_protein) # Build causal steps based on biological knowledge causal_steps = [] # Step 1: Cancer initiation if target_protein.is_oncogene: causal_steps.append(f"Cancer mutations activate {target_protein.gene_name} (oncogene)") else: causal_steps.append(f"Cancer mutations inactivate {target_protein.gene_name} (tumor suppressor)") # Step 2: Pathway dysregulation causal_steps.append(f"Dysregulation in {pathway.name} pathway") # Step 3: Molecular mechanism if pathway.pathway_type == PathwayType.PROLIFERATION: causal_steps.append("Uncontrolled cell proliferation") elif pathway.pathway_type == PathwayType.APOPTOSIS: causal_steps.append("Inhibition of programmed cell death") elif pathway.pathway_type == PathwayType.ANGIOGENESIS: causal_steps.append("Tumor vascularization and growth") # Step 4: Quantum H-bond insight if quantum_analysis.quantum_advantage < -0.1: # Significant quantum effect causal_steps.append(f"Quantum H-bond coherence in {target_protein.gene_name} enables enhanced binding") # Step 5: Therapeutic intervention if drug: causal_steps.append(f"Drug {drug.name} targets {target_protein.gene_name}") if drug.affects_quantum_coherence: causal_steps.append(f"Drug modulates quantum H-bond networks") else: causal_steps.append(f"Therapeutic intervention targeting {target_protein.gene_name}") # Step 6: Cure outcome causal_steps.append("Restoration of normal cellular behavior") causal_steps.append("Tumor regression and patient recovery") # Compress causal chain into TCL expression tcl_expression = self._compress_causal_chain_to_tcl( causal_steps, target_protein, pathway, drug ) # Calculate scores biological_validity = self._calculate_biological_validity( target_protein, pathway, drug, quantum_analysis ) novelty_score = self._calculate_novelty_score( target_protein, pathway, drug, quantum_analysis ) quantum_enhancement = abs(quantum_analysis.quantum_advantage) if quantum_analysis.quantum_advantage < 0 else 0.0 # Extract symbols for the chain context = self.tcl_engine.sessions[self.session_id] symbols = [ context.symbols.symbols.get("Κ"), # Cancer context.symbols.symbols.get("Ω"), # Cure context.symbols.symbols.get("Ψ"), # Protein ] symbols = [s for s in symbols if s is not None] chain = TCLCausalChain( chain_id=chain_id, symbols=symbols, causal_steps=causal_steps, tcl_expression=tcl_expression, quantum_enhancement=quantum_enhancement, biological_validity=biological_validity, novelty_score=novelty_score ) self.causal_chains.append(chain) return chain def _compress_causal_chain_to_tcl(self, causal_steps: List[str], protein: Protein, pathway: CancerPathway, drug: Optional[Drug]) -> str: """Compress causal chain into compact TCL expression""" # Build TCL expression step by step tcl_parts = [] # Start with cancer symbol tcl_parts.append("Κ") # Cancer # Add mutation effect tcl_parts.append("→") tcl_parts.append("Δ") # Mutation # Add protein tcl_parts.append("→") tcl_parts.append(f"Ψ({protein.gene_name})") # Add pathway effect if pathway.pathway_type == PathwayType.PROLIFERATION: tcl_parts.append("→") tcl_parts.append("Π") # Proliferation elif pathway.pathway_type == PathwayType.APOPTOSIS: tcl_parts.append("→") tcl_parts.append("~Α") # Inhibited apoptosis # Add quantum enhancement if relevant quantum_analysis = self.quantum_analyses.get(protein.uniprot_id) if quantum_analysis and quantum_analysis.quantum_advantage < -0.1: tcl_parts.append("∧") tcl_parts.append("Λ(Θ)") # Quantum coherence of H-bonds # Add therapeutic intervention tcl_parts.append("→") if drug: tcl_parts.append(f"Δρ({drug.name})") # Inhibition by drug else: tcl_parts.append("Δρ") # Inhibition # Add apoptosis induction tcl_parts.append("→") tcl_parts.append("Σα") # Activation tcl_parts.append("→") tcl_parts.append("Α") # Apoptosis # End with cure tcl_parts.append("→") tcl_parts.append("Ω") # Cure # Add quantifiers for causality tcl_expression = f"∀x(Δx → Κx ∧ Ψ({protein.gene_name})x)" tcl_expression += f" ∧ ∃y(Δρy → Αy → Ωy)" # Add quantum enhancement if quantum_analysis and quantum_analysis.quantum_advantage < -0.1: tcl_expression += f" ∧ Λ(Θ)" return tcl_expression def _calculate_biological_validity(self, protein: Protein, pathway: CancerPathway, drug: Optional[Drug], quantum_analysis: QuantumProteinAnalysis) -> float: """Calculate how biologically valid a hypothesis is""" validity_score = 0.0 # Protein relevance (high if known oncogene/tumor suppressor) if protein.is_oncogene or protein.is_tumor_suppressor: validity_score += 0.3 else: validity_score += 0.1 # Pathway relevance if pathway.pathway_type in [PathwayType.PROLIFERATION, PathwayType.APOPTOSIS]: validity_score += 0.25 else: validity_score += 0.15 # Drug relevance (higher if FDA approved) if drug: if drug.fda_approved: validity_score += 0.25 elif drug.clinical_status == "clinical_trial": validity_score += 0.15 else: validity_score += 0.05 # Check if drug actually targets this protein if protein.uniprot_id in drug.target_proteins: validity_score += 0.1 # Quantum enhancement (higher if quantum effects are significant) if quantum_analysis.quantum_advantage < -0.1: validity_score += 0.1 return min(1.0, validity_score) def _calculate_novelty_score(self, protein: Protein, pathway: CancerPathway, drug: Optional[Drug], quantum_analysis: QuantumProteinAnalysis) -> float: """Calculate how novel a hypothesis is""" novelty_score = 0.0 # Novelty from quantum H-bond insights (high novelty) if quantum_analysis.quantum_advantage < -0.2: novelty_score += 0.4 elif quantum_analysis.quantum_advantage < -0.1: novelty_score += 0.25 # Novelty from targeting under-explored proteins if drug is None: novelty_score += 0.3 # Novel target elif not drug.fda_approved and drug.clinical_status == "research": novelty_score += 0.25 # Novelty from quantum mechanisms if drug and drug.affects_quantum_coherence: novelty_score += 0.15 # Novelty from pathway combinations if pathway.pathway_type == PathwayType.METABOLISM or pathway.pathway_type == PathwayType.DNA_REPAIR: novelty_score += 0.15 return min(1.0, novelty_score) def get_top_hypotheses(self, n: int = 10) -> List[TCLCausalChain]: """Get top hypotheses by combined score""" scored_chains = [] for chain in self.causal_chains: # Combined score: 60% biological validity + 40% novelty combined_score = (0.6 * chain.biological_validity + 0.4 * chain.novelty_score) scored_chains.append((combined_score, chain)) scored_chains.sort(key=lambda x: x[0], reverse=True) return [chain for score, chain in scored_chains[:n]] def generate_summary_report(self) -> Dict[str, Any]: """Generate comprehensive summary of analysis""" top_hypotheses = self.get_top_hypotheses(5) return { "session_id": self.session_id, "bio_knowledge_stats": self.bio_kb.get_statistics(), "quantum_analyses_performed": len(self.quantum_analyses), "causal_chains_generated": len(self.causal_chains), "avg_biological_validity": sum(c.biological_validity for c in self.causal_chains) / len(self.causal_chains) if self.causal_chains else 0.0, "avg_novelty_score": sum(c.novelty_score for c in self.causal_chains) / len(self.causal_chains) if self.causal_chains else 0.0, "avg_quantum_enhancement": sum(c.quantum_enhancement for c in self.causal_chains) / len(self.causal_chains) if self.causal_chains else 0.0, "top_hypotheses": [h.to_dict() for h in top_hypotheses] }