Employing post-quantum cryptographic primitives to encode and authenticate subgraph isomorphism queries in molecular docking workflows will enable secure, privacy-preserving collaborative drug design across distributed research centers.

Integrating post-quantum cryptographic (PQC) primitives—specifically CRYSTALS-Kyber (key encapsulation) and CRYSTALS-Dilithium (digital signatures) from the NIST PQC standard suite—into subgraph isomorphism query pipelines for molecular docking workflows will: (1) reduce information leakage of proprietary molecular structures to ≤1% of plaintext exposure, (2) authenticate query provenance with ≥99.9% accuracy, (3) maintain end-to-end query latency overhead ≤15% compared to unencrypted baseline workflows, and (4) enable at least 3 geographically distributed research centers to collaboratively screen ≥10,000 ligand-receptor pairs per 24-hour period without exposing raw molecular graph data to any single party.

PHASE 1 — Baseline Characterization (Days 1–15): Establish unencrypted subgraph isomorphism query performance benchmarks using AutoDock Vina with VF2 algorithm on the DUD-E dataset (22,886 ligands across 102 targets). Measure: query latency (ms), throughput (queries/hour), memory footprint (GB), and CPU utilization (%) on standardized 3-node cluster.

PHASE 2 — PQC Integration (Days 16–45): Implement CRYSTALS-Kyber-768 for query encryption and CRYSTALS-Dilithium3 for query signing using liboqs Python bindings. Wrap the VF2 subgraph isomorphism engine with encrypt-then-sign query serialization. Deploy across 3 simulated research center nodes (AWS us-east-1, eu-west-1, ap-southeast-1).

PHASE 3 — Security Validation (Days 46–60): Conduct traffic analysis attacks using Wireshark + custom ML classifier to attempt molecular graph reconstruction from encrypted traffic. Perform timing attack analysis. Engage one external cryptographer for independent audit (40-hour engagement).

PHASE 4 — Performance Benchmarking (Days 61–80): Run identical DUD-E screening campaign with and without PQC layer. Measure all Phase 1 metrics plus: key exchange overhead, signature verification time, ciphertext expansion ratio, and network bandwidth consumption.

PHASE 5 — Collaborative Simulation (Days 81–100): Simulate 90-day drug discovery collaboration compressed into 20-day accelerated trial. Three nodes exchange ≥50,000 encrypted queries. Measure workflow completion rate, user-reported friction (Likert scale survey, n=9 researchers), and data leakage incidents.

# Architecture: PQC-Secured Subgraph Isomorphism Query System
# Components: QueryEncoder, PQCLayer, DistributedOrchestrator, VF2Engine

# === DEPENDENCIES ===
# pip install oqs rdkit-pypi networkx fastapi protobuf scikit-learn numpy

import oqs  # liboqs Python bindings
from rdkit import Chem
from rdkit.Chem import rdmolops
import networkx as nx
import numpy as np
from cryptography.hazmat.primitives.ciphers.aead import AESGCM
import hashlib, os, struct

# === MODULE 1: MOLECULAR GRAPH ENCODER ===
class MolecularGraphEncoder:
    """Converts RDKit molecule to NetworkX graph for subgraph isomorphism."""
    
    def mol_to_graph(self, smiles: str) -> nx.Graph:
        mol = Chem.MolFromSmiles(smiles)
        if mol is None:
            raise ValueError(f"Invalid SMILES: {smiles}")
        G = nx.Graph()
        for atom in mol.GetAtoms():
            G.add_node(atom.GetIdx(), 
                      atomic_num=atom.GetAtomicNum(),
                      formal_charge=atom.GetFormalCharge(),
                      is_aromatic=atom.GetIsAromatic())
        for bond in mol.GetBonds():
            G.add_edge(bond.GetBeginAtomIdx(), bond.GetEndAtomIdx(),
                      bond_type=str(bond.GetBondType()))
        return G
    
    def serialize_query(self, query_graph: nx.Graph, 
                        target_id: str, 
                        requester_id: str) -> bytes:
        """Serialize query to bytes for encryption."""
        import json
        payload = {
            "nodes": list(query_graph.nodes(data=True)),
            "edges": list(query_graph.edges(data=True)),
            "target_id": target_id,
            "requester_id": requester_id,
            "timestamp": int(os.urandom(4).hex(), 16)  # nonce
        }
        return json.dumps(payload, default=str).encode('utf-8')

# === MODULE 2: PQC CRYPTOGRAPHIC LAYER ===
class PQCLayer:
    """Implements Kyber-768 KEM + Dilithium3 signatures."""
    
    KEM_ALG = "Kyber768"
    SIG_ALG = "Dilithium3"
    
    def __init__(self):
        self.kem = oqs.KeyEncapsulation(self.KEM_ALG)
        self.sig = oqs.Signature(self.SIG_ALG)
        self.public_key_kem = self.kem.generate_keypair()
        self.public_key_sig = self.sig.generate_keypair()
    
    def encrypt_query(self, plaintext: bytes, 
                      recipient_public_key: bytes) -> dict:
        """Encrypt-then-sign query for recipient."""
        # Step 1: KEM encapsulation → shared secret
        ciphertext_kem, shared_secret = self.kem.encap_secret(
            recipient_public_key
        )
        # Step 2: Derive AES-256 key from shared secret
        aes_key = hashlib.sha256(shared_secret).digest()  # 32 bytes
        # Step 3: AES-256-GCM encryption
        aesgcm = AESGCM(aes_key)
        nonce = os.urandom(12)
        ciphertext_data = aesgcm.encrypt(nonce, plaintext, None)
        # Step 4: Sign {kem_ciphertext || nonce || ciphertext_data}
        message_to_sign = ciphertext_kem + nonce + ciphertext_data
        signature = self.sig.sign(message_to_sign)
        return {
            "ciphertext_kem": ciphertext_kem,
            "nonce": nonce,
            "ciphertext_data": ciphertext_data,
            "signature": signature,
            "sender_sig_pubkey": self.public_key_sig
        }
    
    def decrypt_and_verify(self, encrypted_package: dict,
                            sender_sig_pubkey: bytes) -> bytes:
        """Verify signature then decrypt query."""
        # Step 1: Verify signature
        message_to_verify = (encrypted_package["ciphertext_kem"] + 
                             encrypted_package["nonce"] + 
                             encrypted_package["ciphertext_data"])
        verifier = oqs.Signature(self.SIG_ALG)
        is_valid = verifier.verify(
            message_to_verify,
            encrypted_package["signature"],
            sender_sig_pubkey
        )
        if not is_valid:
            raise SecurityError("Signature verification FAILED — query rejected")
        # Step 2: KEM decapsulation
        shared_secret = self.kem.decap_secret(
            encrypted_package["ciphertext_kem"]
        )
        # Step 3: AES-256-GCM decryption
        aes_key = hashlib.sha256(shared_secret).digest()
        aesgcm = AESGCM(aes_key)
        plaintext = aesgcm.decrypt(
            encrypted_package["nonce"],
            encrypted_package["ciphertext_data"],
            None
        )
        return plaintext

# === MODULE 3: VF2 SUBGRAPH ISOMORPHISM ENGINE ===
class SubgraphIsomorphismEngine:
    """Executes VF2 subgraph isomorphism queries on molecular graphs."""
    
    def __init__(self, target_graph_db: dict):
        # target_graph_db: {target_id: nx.Graph}
        self.db = target_graph_db
    
    def node_match(self, n1_attrs, n2_attrs) -> bool:
        return n1_attrs.get('atomic_num') == n2_attrs.get('atomic_num')
    
    def edge_match(self, e1_attrs, e2_attrs) -> bool:
        return e1_attrs.get('bond_type') == e2_attrs.get('bond_type')
    
    def query(self, query_graph: nx.Graph, target_id: str) -> dict:
        if target_id not in self.db:
            return {"found": False, "error": "Target not found"}
        target_graph = self.db[target_id]
        GM = nx.algorithms.isomorphism.GraphMatcher(
            target_graph, query_graph,
            node_match=self.node_match,
            edge_match=self.edge_match
        )
        is_subgraph = GM.subgraph_is_isomorphic()
        mappings = list(GM.subgraph_isomorphisms_iter()) if is_subgraph else []
        return {
            "found": is_subgraph,
            "num_mappings": len(mappings),
            "target_id": target_id
        }

# === MODULE 4: DISTRIBUTED ORCHESTRATOR ===
class DistributedOrchestrator:
    """Coordinates encrypted query distribution across research center nodes."""
    
    def __init__(self, node_configs: list):
        # node_configs: [{"node_id": str, "endpoint": str, "pubkey_kem": bytes}]
        self.nodes = {cfg["node_id"]: cfg for cfg in node_configs}
        self.pqc = PQCLayer()
        self.encoder = MolecularGraphEncoder()
        self.query_log = []  # Audit trail
    
    def submit_query(self, query_smiles: str, 
                     target_id: str,
                     target_node_id: str) -> dict:
        """Encode, encrypt, sign, and submit query to target node."""
        # Encode molecular query
        query_graph = self.encoder.mol_to_graph(query_smiles)
        serialized = self.encoder.serialize_query(
            query_graph, target_id, "local_node"
        )
        # Encrypt for target node
        target_pubkey = self.nodes[target_node_id]["pubkey_kem"]
        encrypted_pkg = self.pqc.encrypt_query(serialized, target_pubkey)
        # Log query hash (not content) for audit trail
        query_hash = hashlib.sha3_256(serialized).hexdigest()
        self.query_log.append({
            "hash": query_hash,
            "target_node": target_node_id,
            "target_id": target_id
        })
        # Transmit (HTTP POST to node endpoint)
        return self._transmit(encrypted_pkg, target_node_id)
    
    def _transmit(self, encrypted_pkg: dict, node_id: str) -> dict:
        """Simulate or execute HTTP transmission to remote node."""
        import requests
        endpoint = self.nodes[node_id]["endpoint"]
        # Serialize bytes fields to base64 for JSON transport
        import base64
        payload = {k: base64.b64encode(v).decode() 
                   if isinstance(v, bytes) else v 
                   for k, v in encrypted_pkg.items()}
        response = requests.post(f"{endpoint}/query", json=payload, timeout=30)
        return response.json()

# === MODULE 5: BENCHMARK HARNESS ===
class BenchmarkHarness:
    """Measures latency, throughput, and overhead metrics."""
    
    def run_benchmark(self, queries: list, 
                      orchestrator: DistributedOrchestrator,
                      mode: str = "pqc") -> dict:
        import time
        latencies = []
        for smiles, target_id, node_id in queries:
            t0 = time.perf_counter()
            if mode == "pqc":
                result = orchestrator.submit_query(smiles, target_id, node_id)
            else:
                # Baseline: direct unencrypted query
                result = orchestrator._submit_plaintext(smiles, target_id, node_id)
            t1 = time.perf_counter()
            latencies.append((t1 - t0) * 1000)  # ms
        return {
            "mean_latency_ms": np.mean(latencies),
            "p95_latency_ms": np.percentile(latencies, 95),
            "p99_latency_ms": np.percentile(latencies, 99),
            "throughput_qps": len(queries) / (sum(latencies) / 1000),
            "mode": mode
        }
    
    def compute_overhead(self, pqc_metrics: dict, 
                         baseline_metrics: dict) -> dict:
        overhead_pct = ((pqc_metrics["mean_latency_ms"] - 
                         baseline_metrics["mean_latency_ms"]) / 
                        baseline_metrics["mean_latency_ms"]) * 100
        return {
            "latency_overhead_pct": overhead_pct,
            "throughput_ratio": (pqc_metrics["throughput_qps"] / 
                                baseline_metrics["throughput_qps"]),
            "passes_threshold": overhead_pct <= 15.0
        }

# === SECURITY ANALYSIS MODULE ===
class TrafficAnalysisAttack:
    """Attempts to reconstruct molecular properties from encrypted traffic."""
    
    def extract_features(self, packet_capture: list) -> np.ndarray:
        """Extract traffic metadata features (no payload inspection)."""
        features = []
        for flow in packet_capture:
            features.append([
                flow["packet_size"],
                flow["inter_arrival_time_ms"],
                flow["flow_duration_ms"],
                flow["num_packets"],
                flow["bytes_per_second"]
            ])
        return np.array(features)
    
    def attack(self, features: np.ndarray, 
               labels: np.ndarray) -> dict:
        """Train RF classifier to predict molecular properties."""
        from sklearn.ensemble import RandomForestClassifier
        from sklearn.model_selection import cross_val_score
        from sklearn.metrics import roc_auc_score
        clf = RandomForestClassifier(n_estimators=100, random_state=42)
        auc_scores = cross_val_score(clf, features, labels, 
                                     cv=5, scoring='roc_auc')