quantrs2-anneal 0.1.3

//! Drug Discovery Molecular Optimization with Advanced Quantum Algorithms
//!
//! This module implements cutting-edge drug discovery optimization using quantum annealing
//! with integrated quantum error correction and advanced algorithms. It addresses fundamental
//! pharmaceutical challenges including molecular design, drug-target interaction optimization,
//! ADMET property prediction, and multi-objective drug development.
//!
//! Key Features:
//! - Molecular graph representation and SMILES encoding
//! - Drug-target binding affinity optimization
//! - Multi-objective optimization (efficacy, safety, synthesizability)
//! - ADMET property prediction and optimization
//! - Fragment-based drug design
//! - Lead compound optimization with quantum algorithms
//! - Structure-Activity Relationship (SAR) modeling
//! - Quantum molecular descriptors and property calculation
//! - Advanced quantum error correction for molecular simulations

use std::collections::{HashMap, HashSet, VecDeque};
use std::fmt;

use crate::advanced_quantum_algorithms::{
    AdiabaticShortcutsOptimizer, AdvancedAlgorithmConfig, AdvancedQuantumAlgorithms,
    AlgorithmSelectionStrategy, InfiniteDepthQAOA, InfiniteQAOAConfig, QuantumZenoAnnealer,
    ShortcutsConfig, ZenoConfig,
};
use crate::applications::{
    ApplicationError, ApplicationResult, IndustrySolution, OptimizationProblem,
};
use crate::bayesian_hyperopt::{optimize_annealing_parameters, BayesianHyperoptimizer};
use crate::ising::{IsingModel, QuboModel};
use crate::neural_annealing_schedules::{NeuralAnnealingScheduler, NeuralSchedulerConfig};
use crate::quantum_error_correction::{
    ErrorCorrectionCode, ErrorMitigationConfig, ErrorMitigationManager, LogicalAnnealingEncoder,
    NoiseResilientAnnealingProtocol, SyndromeDetector,
};
use crate::simulator::{AnnealingParams, AnnealingResult, QuantumAnnealingSimulator};
use std::fmt::Write;

/// Chemical elements for molecular representation
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum AtomType {
    Hydrogen,
    Carbon,
    Nitrogen,
    Oxygen,
    Phosphorus,
    Sulfur,
    Fluorine,
    Chlorine,
    Bromine,
    Iodine,
    Custom(u8), // For other elements
}

impl AtomType {
    #[must_use]
    pub const fn atomic_number(&self) -> u8 {
        match self {
            Self::Hydrogen => 1,
            Self::Carbon => 6,
            Self::Nitrogen => 7,
            Self::Oxygen => 8,
            Self::Phosphorus => 15,
            Self::Sulfur => 16,
            Self::Fluorine => 9,
            Self::Chlorine => 17,
            Self::Bromine => 35,
            Self::Iodine => 53,
            Self::Custom(n) => *n,
        }
    }

    #[must_use]
    pub const fn symbol(&self) -> &'static str {
        match self {
            Self::Hydrogen => "H",
            Self::Carbon => "C",
            Self::Nitrogen => "N",
            Self::Oxygen => "O",
            Self::Phosphorus => "P",
            Self::Sulfur => "S",
            Self::Fluorine => "F",
            Self::Chlorine => "Cl",
            Self::Bromine => "Br",
            Self::Iodine => "I",
            Self::Custom(_) => "X",
        }
    }

    #[must_use]
    pub const fn valence(&self) -> u8 {
        match self {
            Self::Hydrogen | Self::Fluorine | Self::Chlorine => 1,
            Self::Carbon => 4,
            Self::Nitrogen => 3,
            Self::Oxygen => 2,
            Self::Phosphorus => 5,
            Self::Sulfur => 6,
            Self::Bromine => 1,
            Self::Iodine => 1,
            Self::Custom(_) => 4, // Default
        }
    }
}

/// Atomic hybridization states
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum Hybridization {
    SP,
    SP2,
    SP3,
    SP3D,
    SP3D2,
}

/// 3D position coordinates
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct Position3D {
    pub x: f64,
    pub y: f64,
    pub z: f64,
}

impl Position3D {
    #[must_use]
    pub const fn new(x: f64, y: f64, z: f64) -> Self {
        Self { x, y, z }
    }

    #[must_use]
    pub fn distance(&self, other: &Self) -> f64 {
        (self.z - other.z)
            .mul_add(
                self.z - other.z,
                (self.y - other.y).mul_add(self.y - other.y, (self.x - other.x).powi(2)),
            )
            .sqrt()
    }
}

/// Bond types in molecular graphs
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum BondType {
    Single,
    Double,
    Triple,
    Aromatic,
}

impl BondType {
    #[must_use]
    pub const fn bond_order(&self) -> f64 {
        match self {
            Self::Single => 1.0,
            Self::Double => 2.0,
            Self::Triple => 3.0,
            Self::Aromatic => 1.5,
        }
    }
}

/// Molecular atom with properties
#[derive(Debug, Clone)]
pub struct Atom {
    pub id: usize,
    pub atom_type: AtomType,
    pub formal_charge: i8,
    pub hybridization: Option<String>,
    pub aromatic: bool,
    pub coordinates: Option<[f64; 3]>, // 3D coordinates if available
}

impl Atom {
    #[must_use]
    pub const fn new(id: usize, atom_type: AtomType) -> Self {
        Self {
            id,
            atom_type,
            formal_charge: 0,
            hybridization: None,
            aromatic: false,
            coordinates: None,
        }
    }

    #[must_use]
    pub const fn with_charge(mut self, charge: i8) -> Self {
        self.formal_charge = charge;
        self
    }

    #[must_use]
    pub const fn with_coordinates(mut self, coords: [f64; 3]) -> Self {
        self.coordinates = Some(coords);
        self
    }

    #[must_use]
    pub const fn set_aromatic(mut self, aromatic: bool) -> Self {
        self.aromatic = aromatic;
        self
    }
}

/// Chemical bond between atoms
#[derive(Debug, Clone)]
pub struct Bond {
    pub atom1: usize,
    pub atom2: usize,
    pub bond_type: BondType,
    pub aromatic: bool,
    pub in_ring: bool,
}

impl Bond {
    #[must_use]
    pub const fn new(atom1: usize, atom2: usize, bond_type: BondType) -> Self {
        Self {
            atom1,
            atom2,
            bond_type,
            aromatic: false,
            in_ring: false,
        }
    }

    #[must_use]
    pub const fn set_aromatic(mut self, aromatic: bool) -> Self {
        self.aromatic = aromatic;
        self
    }

    #[must_use]
    pub const fn set_in_ring(mut self, in_ring: bool) -> Self {
        self.in_ring = in_ring;
        self
    }
}

/// Molecular graph representation
#[derive(Debug, Clone)]
pub struct Molecule {
    pub id: String,
    pub atoms: Vec<Atom>,
    pub bonds: Vec<Bond>,
    pub smiles: Option<String>,
    pub properties: HashMap<String, f64>,
}

impl Molecule {
    #[must_use]
    pub fn new(id: String) -> Self {
        Self {
            id,
            atoms: Vec::new(),
            bonds: Vec::new(),
            smiles: None,
            properties: HashMap::new(),
        }
    }

    pub fn add_atom(&mut self, atom: Atom) -> usize {
        let id = self.atoms.len();
        self.atoms.push(atom);
        id
    }

    pub fn add_bond(&mut self, bond: Bond) {
        self.bonds.push(bond);
    }

    pub fn set_smiles(&mut self, smiles: String) {
        self.smiles = Some(smiles);
    }

    pub fn set_property(&mut self, name: String, value: f64) {
        self.properties.insert(name, value);
    }

    /// Calculate molecular weight
    #[must_use]
    pub fn molecular_weight(&self) -> f64 {
        self.atoms
            .iter()
            .map(|atom| {
                match atom.atom_type {
                    AtomType::Hydrogen => 1.008,
                    AtomType::Carbon => 12.011,
                    AtomType::Nitrogen => 14.007,
                    AtomType::Oxygen => 15.999,
                    AtomType::Phosphorus => 30.974,
                    AtomType::Sulfur => 32.065,
                    AtomType::Fluorine => 18.998,
                    AtomType::Chlorine => 35.453,
                    AtomType::Bromine => 79.904,
                    AtomType::Iodine => 126.904,
                    AtomType::Custom(_) => 12.0, // Default
                }
            })
            .sum()
    }

    /// Calculate `LogP` (lipophilicity) approximation
    #[must_use]
    pub fn logp_approximation(&self) -> f64 {
        let mut logp = 0.0;

        // Simplified LogP calculation based on atom contributions
        for atom in &self.atoms {
            match atom.atom_type {
                AtomType::Carbon => logp += 0.5,
                AtomType::Nitrogen => logp -= 0.5,
                AtomType::Oxygen => logp -= 1.0,
                AtomType::Sulfur => logp += 0.2,
                AtomType::Fluorine => logp += 0.1,
                AtomType::Chlorine => logp += 0.7,
                AtomType::Bromine => logp += 1.0,
                _ => {}
            }
        }

        logp
    }

    /// Calculate topological polar surface area (TPSA) approximation
    #[must_use]
    pub fn tpsa_approximation(&self) -> f64 {
        let mut tpsa = 0.0;

        for atom in &self.atoms {
            match atom.atom_type {
                AtomType::Nitrogen => tpsa += 23.79,
                AtomType::Oxygen => tpsa += 23.06,
                _ => {}
            }
        }

        tpsa
    }

    /// Count rotatable bonds
    #[must_use]
    pub fn rotatable_bonds(&self) -> usize {
        self.bonds
            .iter()
            .filter(|bond| {
                bond.bond_type == BondType::Single && !bond.in_ring && !self.is_terminal_bond(bond)
            })
            .count()
    }

    fn is_terminal_bond(&self, bond: &Bond) -> bool {
        let atom1_degree = self
            .bonds
            .iter()
            .filter(|b| b.atom1 == bond.atom1 || b.atom2 == bond.atom2)
            .count();
        let atom2_degree = self
            .bonds
            .iter()
            .filter(|b| b.atom1 == bond.atom1 || b.atom2 == bond.atom2)
            .count();
        atom1_degree == 1 || atom2_degree == 1
    }

    /// Calculate drug-likeness score (Lipinski's Rule of Five compliance)
    #[must_use]
    pub fn drug_likeness_score(&self) -> f64 {
        let mw = self.molecular_weight();
        let logp = self.logp_approximation();
        let tpsa = self.tpsa_approximation();
        let rot_bonds = self.rotatable_bonds() as f64;

        let mut score = 1.0;

        // Molecular weight penalty
        if mw > 500.0 {
            score *= 0.5;
        }

        // LogP penalty
        if logp > 5.0 || logp < -2.0 {
            score *= 0.5;
        }

        // TPSA penalty
        if tpsa > 140.0 {
            score *= 0.7;
        }

        // Rotatable bonds penalty
        if rot_bonds > 10.0 {
            score *= 0.8;
        }

        score
    }
}

/// Drug-target interaction representation
#[derive(Debug, Clone)]
pub struct DrugTargetInteraction {
    pub drug_molecule: Molecule,
    pub target_id: String,
    pub target_type: TargetType,
    pub binding_affinity: Option<f64>,     // pKd or pIC50
    pub selectivity: HashMap<String, f64>, // Off-target interactions
    pub admet_properties: AdmetProperties,
}

/// Types of drug targets
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum TargetType {
    Enzyme,
    Receptor,
    IonChannel,
    Transporter,
    StructuralProtein,
    Other(String),
}

/// ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties
#[derive(Debug, Clone)]
pub struct AdmetProperties {
    pub absorption: AdsorptionProperties,
    pub distribution: DistributionProperties,
    pub metabolism: MetabolismProperties,
    pub excretion: ExcretionProperties,
    pub toxicity: ToxicityProperties,
}

#[derive(Debug, Clone)]
pub struct AdsorptionProperties {
    pub bioavailability: Option<f64>,
    pub permeability: Option<f64>,
    pub solubility: Option<f64>,
}

#[derive(Debug, Clone)]
pub struct DistributionProperties {
    pub volume_distribution: Option<f64>,
    pub protein_binding: Option<f64>,
    pub blood_brain_barrier: Option<f64>,
}

#[derive(Debug, Clone)]
pub struct MetabolismProperties {
    pub clearance: Option<f64>,
    pub half_life: Option<f64>,
    pub cyp_inhibition: HashMap<String, f64>,
}

#[derive(Debug, Clone)]
pub struct ExcretionProperties {
    pub renal_clearance: Option<f64>,
    pub biliary_excretion: Option<f64>,
}

#[derive(Debug, Clone)]
pub struct ToxicityProperties {
    pub cytotoxicity: Option<f64>,
    pub hepatotoxicity: Option<f64>,
    pub cardiotoxicity: Option<f64>,
    pub mutagenicity: Option<f64>,
}

impl Default for AdmetProperties {
    fn default() -> Self {
        Self {
            absorption: AdsorptionProperties {
                bioavailability: None,
                permeability: None,
                solubility: None,
            },
            distribution: DistributionProperties {
                volume_distribution: None,
                protein_binding: None,
                blood_brain_barrier: None,
            },
            metabolism: MetabolismProperties {
                clearance: None,
                half_life: None,
                cyp_inhibition: HashMap::new(),
            },
            excretion: ExcretionProperties {
                renal_clearance: None,
                biliary_excretion: None,
            },
            toxicity: ToxicityProperties {
                cytotoxicity: None,
                hepatotoxicity: None,
                cardiotoxicity: None,
                mutagenicity: None,
            },
        }
    }
}

/// Drug discovery optimization objectives
#[derive(Debug, Clone)]
pub enum DrugOptimizationObjective {
    /// Maximize binding affinity to target
    MaximizeAffinity,
    /// Minimize off-target effects
    MinimizeOffTarget,
    /// Maximize drug-likeness
    MaximizeDrugLikeness,
    /// Minimize toxicity
    MinimizeToxicity,
    /// Maximize synthesizability
    MaximizeSynthesizability,
    /// Optimize ADMET properties
    OptimizeAdmet,
    /// Multi-objective combination
    MultiObjective(Vec<(Self, f64)>),
}

/// Drug discovery optimization problem
#[derive(Debug, Clone)]
pub struct DrugDiscoveryProblem {
    /// Target molecule or interaction
    pub target_interaction: DrugTargetInteraction,
    /// Optimization objectives
    pub objectives: Vec<DrugOptimizationObjective>,
    /// Molecular constraints
    pub constraints: Vec<MolecularConstraint>,
    /// Quantum error correction framework
    pub qec_framework: Option<String>,
    /// Advanced algorithm configuration
    pub advanced_config: AdvancedAlgorithmConfig,
    /// Neural scheduling configuration
    pub neural_config: Option<NeuralSchedulerConfig>,
}

/// Molecular design constraints
#[derive(Debug, Clone)]
pub enum MolecularConstraint {
    /// Molecular weight range
    MolecularWeightRange { min: f64, max: f64 },
    /// `LogP` range for lipophilicity
    LogPRange { min: f64, max: f64 },
    /// TPSA constraint
    TpsaLimit(f64),
    /// Maximum rotatable bonds
    MaxRotatableBonds(usize),
    /// Required functional groups
    RequiredGroups(Vec<String>),
    /// Forbidden substructures
    ForbiddenSubstructures(Vec<String>),
    /// Synthesizability score minimum
    MinSynthesizability(f64),
    /// Maximum heavy atoms
    MaxHeavyAtoms(usize),
}

impl DrugDiscoveryProblem {
    #[must_use]
    pub fn new(target_interaction: DrugTargetInteraction) -> Self {
        Self {
            target_interaction,
            objectives: vec![DrugOptimizationObjective::MaximizeAffinity],
            constraints: vec![],
            qec_framework: None,
            advanced_config: AdvancedAlgorithmConfig {
                enable_infinite_qaoa: true,
                enable_zeno_annealing: true,
                enable_adiabatic_shortcuts: true,
                enable_counterdiabatic: true,
                selection_strategy: AlgorithmSelectionStrategy::ProblemSpecific,
                track_performance: true,
            },
            neural_config: None,
        }
    }

    #[must_use]
    pub fn with_quantum_error_correction(mut self, config: String) -> Self {
        self.qec_framework = Some(config);
        self
    }

    #[must_use]
    pub fn with_neural_annealing(mut self, config: NeuralSchedulerConfig) -> Self {
        self.neural_config = Some(config);
        self
    }

    #[must_use]
    pub fn add_objective(mut self, objective: DrugOptimizationObjective) -> Self {
        self.objectives.push(objective);
        self
    }

    #[must_use]
    pub fn add_constraint(mut self, constraint: MolecularConstraint) -> Self {
        self.constraints.push(constraint);
        self
    }

    /// Solve using infinite-depth QAOA
    pub fn solve_with_infinite_qaoa(&self) -> ApplicationResult<Molecule> {
        println!("Starting drug discovery optimization with infinite-depth QAOA");

        let (ising_model, variable_map) = self.to_ising_model()?;

        let qaoa_config = InfiniteQAOAConfig {
            max_depth: 50,
            initial_depth: 3,
            optimization_tolerance: 1e-8,
            ..Default::default()
        };

        let mut qaoa = InfiniteDepthQAOA::new(qaoa_config);
        let result = qaoa.solve(&ising_model).map_err(|e| {
            ApplicationError::OptimizationError(format!("Infinite QAOA failed: {e:?}"))
        })?;

        let solution = result
            .map_err(|e| ApplicationError::OptimizationError(format!("QAOA solver error: {e}")))?;

        self.solution_to_molecule(&solution, &variable_map)
    }

    /// Solve using Quantum Zeno annealing
    pub fn solve_with_zeno_annealing(&self) -> ApplicationResult<Molecule> {
        println!("Starting drug discovery optimization with Quantum Zeno annealing");

        let (ising_model, variable_map) = self.to_ising_model()?;

        let zeno_config = ZenoConfig {
            measurement_frequency: 2.0,
            total_evolution_time: 20.0,
            evolution_time_step: 0.05,
            ..Default::default()
        };

        let mut zeno_annealer = QuantumZenoAnnealer::new(zeno_config);
        let result = zeno_annealer.solve(&ising_model).map_err(|e| {
            ApplicationError::OptimizationError(format!("Zeno annealing failed: {e:?}"))
        })?;

        let solution = result
            .map_err(|e| ApplicationError::OptimizationError(format!("Zeno solver error: {e}")))?;

        self.solution_to_molecule(&solution, &variable_map)
    }

    /// Solve using adiabatic shortcuts
    pub fn solve_with_adiabatic_shortcuts(&self) -> ApplicationResult<Molecule> {
        println!("Starting drug discovery optimization with adiabatic shortcuts");

        let (ising_model, variable_map) = self.to_ising_model()?;

        let shortcuts_config = ShortcutsConfig::default();
        let mut shortcuts_optimizer = AdiabaticShortcutsOptimizer::new(shortcuts_config);

        let result = shortcuts_optimizer.solve(&ising_model).map_err(|e| {
            ApplicationError::OptimizationError(format!("Adiabatic shortcuts failed: {e:?}"))
        })?;

        let solution = result.map_err(|e| {
            ApplicationError::OptimizationError(format!("Shortcuts solver error: {e}"))
        })?;

        self.solution_to_molecule(&solution, &variable_map)
    }

    /// Solve with quantum error correction
    pub fn solve_with_error_correction(&self) -> ApplicationResult<Molecule> {
        if let Some(ref qec_framework) = self.qec_framework {
            println!("Starting noise-resilient drug discovery optimization");

            let (ising_model, variable_map) = self.to_ising_model()?;

            // Use error mitigation for molecular optimization
            let error_config = ErrorMitigationConfig::default();
            let mut error_manager = ErrorMitigationManager::new(error_config).map_err(|e| {
                ApplicationError::OptimizationError(format!(
                    "Failed to create error manager: {e:?}"
                ))
            })?;

            // First perform standard annealing
            let params = AnnealingParams::default();
            let annealer = QuantumAnnealingSimulator::new(params.clone()).map_err(|e| {
                ApplicationError::OptimizationError(format!("Failed to create annealer: {e:?}"))
            })?;
            let annealing_result = annealer.solve(&ising_model).map_err(|e| {
                ApplicationError::OptimizationError(format!("Annealing failed: {e:?}"))
            })?;

            // Convert simulator result to error mitigation format
            let error_mitigation_result =
                crate::quantum_error_correction::error_mitigation::AnnealingResult {
                    solution: annealing_result
                        .best_spins
                        .iter()
                        .map(|&x| i32::from(x))
                        .collect(),
                    energy: annealing_result.best_energy,
                    num_occurrences: 1,
                    chain_break_fraction: 0.0,
                    timing: std::collections::HashMap::new(),
                    info: std::collections::HashMap::new(),
                };

            // Apply error mitigation to improve the result
            let mitigation_result = error_manager
                .apply_mitigation(&ising_model, error_mitigation_result, &params)
                .map_err(|e| {
                    ApplicationError::OptimizationError(format!("Error mitigation failed: {e:?}"))
                })?;

            let solution = &mitigation_result.mitigated_result.solution;

            self.solution_to_molecule(solution, &variable_map)
        } else {
            Err(ApplicationError::InvalidConfiguration(
                "Quantum error correction not enabled".to_string(),
            ))
        }
    }

    /// Optimize molecular parameters using Bayesian optimization
    pub fn optimize_molecular_parameters(&self) -> ApplicationResult<HashMap<String, f64>> {
        println!("Optimizing molecular parameters with Bayesian optimization");

        let objective = |params: &[f64]| -> f64 {
            // params[0] = molecular weight target, params[1] = logP target, params[2] = TPSA target
            let mw_target = params[0];
            let logp_target = params[1];
            let tpsa_target = params[2];

            let current_mw = self.target_interaction.drug_molecule.molecular_weight();
            let current_logp = self.target_interaction.drug_molecule.logp_approximation();
            let current_tpsa = self.target_interaction.drug_molecule.tpsa_approximation();

            // Calculate optimization score based on distance from targets
            let mw_score = -((current_mw - mw_target) / 100.0).powi(2);
            let logp_score = -((current_logp - logp_target) / 2.0).powi(2);
            let tpsa_score = -((current_tpsa - tpsa_target) / 50.0).powi(2);

            // Combined score with drug-likeness
            let drug_likeness = self.target_interaction.drug_molecule.drug_likeness_score();

            mw_score + logp_score + tpsa_score + drug_likeness
        };

        let best_params = optimize_annealing_parameters(objective, Some(40)).map_err(|e| {
            ApplicationError::OptimizationError(format!("Bayesian optimization failed: {e:?}"))
        })?;

        let mut result = HashMap::new();
        result.insert("optimal_molecular_weight".to_string(), best_params[0]);
        result.insert("optimal_logp".to_string(), best_params[1]);
        result.insert("optimal_tpsa".to_string(), best_params[2]);

        Ok(result)
    }

    /// Convert to Ising model representation
    fn to_ising_model(&self) -> ApplicationResult<(IsingModel, HashMap<String, usize>)> {
        let num_atoms = self.target_interaction.drug_molecule.atoms.len();
        let num_variables = num_atoms * 8; // 8 bits per atom for type encoding

        let mut ising = IsingModel::new(num_variables);
        let mut variable_map = HashMap::new();

        // Map molecular variables to Ising variables
        for (i, atom) in self
            .target_interaction
            .drug_molecule
            .atoms
            .iter()
            .enumerate()
        {
            for bit in 0..8 {
                variable_map.insert(format!("atom_{i}_bit_{bit}"), i * 8 + bit);
            }
        }

        // Add objective-based bias terms
        for (i, atom) in self
            .target_interaction
            .drug_molecule
            .atoms
            .iter()
            .enumerate()
        {
            for bit in 0..8 {
                let var_idx = i * 8 + bit;
                let mut bias = 0.0;

                // Add objective-specific bias
                for objective in &self.objectives {
                    match objective {
                        DrugOptimizationObjective::MaximizeAffinity => {
                            // Encourage beneficial atom types for binding
                            match atom.atom_type {
                                AtomType::Nitrogen | AtomType::Oxygen => bias -= 0.5,
                                AtomType::Carbon => bias -= 0.2,
                                _ => bias += 0.1,
                            }
                        }
                        DrugOptimizationObjective::MaximizeDrugLikeness => {
                            // Encourage drug-like properties
                            bias -=
                                self.target_interaction.drug_molecule.drug_likeness_score() * 0.1;
                        }
                        DrugOptimizationObjective::MinimizeToxicity => {
                            // Penalize potentially toxic groups
                            if matches!(atom.atom_type, AtomType::Bromine | AtomType::Iodine) {
                                bias += 1.0;
                            }
                        }
                        _ => {
                            bias += 0.05; // Default small bias
                        }
                    }
                }

                ising
                    .set_bias(var_idx, bias)
                    .map_err(|e| ApplicationError::OptimizationError(e.to_string()))?;
            }
        }

        // Add coupling terms for molecular connectivity
        for bond in &self.target_interaction.drug_molecule.bonds {
            let atom1_base = bond.atom1 * 8;
            let atom2_base = bond.atom2 * 8;

            for bit1 in 0..8 {
                for bit2 in 0..8 {
                    let var1 = atom1_base + bit1;
                    let var2 = atom2_base + bit2;

                    if var1 < var2 && var1 < num_variables && var2 < num_variables {
                        let coupling = bond.bond_type.bond_order() * 0.1;
                        ising
                            .set_coupling(var1, var2, -coupling)
                            .map_err(|e| ApplicationError::OptimizationError(e.to_string()))?;
                        // Negative for stability
                    }
                }
            }
        }

        Ok((ising, variable_map))
    }

    /// Convert Ising solution back to molecular structure
    fn solution_to_molecule(
        &self,
        solution: &[i32],
        variable_map: &HashMap<String, usize>,
    ) -> ApplicationResult<Molecule> {
        let mut optimized_molecule = self.target_interaction.drug_molecule.clone();

        // Update molecular structure based on solution
        for (i, atom) in optimized_molecule.atoms.iter_mut().enumerate() {
            // Decode atom type from binary solution
            let mut atom_encoding = 0u8;

            for bit in 0..8 {
                if let Some(&var_index) = variable_map.get(&format!("atom_{i}_bit_{bit}")) {
                    if var_index < solution.len() && solution[var_index] > 0 {
                        atom_encoding |= 1 << bit;
                    }
                }
            }

            // Update atom type based on encoding
            atom.atom_type = match atom_encoding % 10 {
                0 => AtomType::Carbon,
                1 => AtomType::Nitrogen,
                2 => AtomType::Oxygen,
                3 => AtomType::Sulfur,
                4 => AtomType::Phosphorus,
                5 => AtomType::Fluorine,
                6 => AtomType::Chlorine,
                7 => AtomType::Hydrogen,
                _ => atom.atom_type, // Keep original
            };
        }

        // Recalculate molecular properties
        optimized_molecule.set_property(
            "molecular_weight".to_string(),
            optimized_molecule.molecular_weight(),
        );
        optimized_molecule
            .set_property("logp".to_string(), optimized_molecule.logp_approximation());
        optimized_molecule
            .set_property("tpsa".to_string(), optimized_molecule.tpsa_approximation());
        optimized_molecule.set_property(
            "drug_likeness".to_string(),
            optimized_molecule.drug_likeness_score(),
        );

        Ok(optimized_molecule)
    }
}

impl OptimizationProblem for DrugDiscoveryProblem {
    type Solution = Molecule;
    type ObjectiveValue = f64;

    fn description(&self) -> String {
        format!(
            "Drug discovery optimization for {} targeting {} (MW: {:.2})",
            self.target_interaction.drug_molecule.id,
            self.target_interaction.target_id,
            self.target_interaction.drug_molecule.molecular_weight()
        )
    }

    fn size_metrics(&self) -> HashMap<String, usize> {
        let mut metrics = HashMap::new();
        metrics.insert(
            "num_atoms".to_string(),
            self.target_interaction.drug_molecule.atoms.len(),
        );
        metrics.insert(
            "num_bonds".to_string(),
            self.target_interaction.drug_molecule.bonds.len(),
        );
        metrics.insert(
            "rotatable_bonds".to_string(),
            self.target_interaction.drug_molecule.rotatable_bonds(),
        );
        metrics.insert(
            "variables".to_string(),
            self.target_interaction.drug_molecule.atoms.len() * 8,
        );
        metrics
    }

    fn validate(&self) -> ApplicationResult<()> {
        if self.target_interaction.drug_molecule.atoms.is_empty() {
            return Err(ApplicationError::DataValidationError(
                "Molecule must have at least one atom".to_string(),
            ));
        }

        if self.target_interaction.drug_molecule.atoms.len() > 100 {
            return Err(ApplicationError::ResourceLimitExceeded(
                "Molecule too large for current implementation".to_string(),
            ));
        }

        // Validate molecular constraints
        for constraint in &self.constraints {
            match constraint {
                MolecularConstraint::MolecularWeightRange { min, max } => {
                    let mw = self.target_interaction.drug_molecule.molecular_weight();
                    if mw < *min || mw > *max {
                        return Err(ApplicationError::ConstraintViolation(format!(
                            "Molecular weight {mw} outside range [{min}, {max}]"
                        )));
                    }
                }
                MolecularConstraint::LogPRange { min, max } => {
                    let logp = self.target_interaction.drug_molecule.logp_approximation();
                    if logp < *min || logp > *max {
                        return Err(ApplicationError::ConstraintViolation(format!(
                            "LogP {logp} outside range [{min}, {max}]"
                        )));
                    }
                }
                MolecularConstraint::MaxHeavyAtoms(max_atoms) => {
                    let heavy_atoms = self
                        .target_interaction
                        .drug_molecule
                        .atoms
                        .iter()
                        .filter(|atom| atom.atom_type != AtomType::Hydrogen)
                        .count();
                    if heavy_atoms > *max_atoms {
                        return Err(ApplicationError::ConstraintViolation(format!(
                            "Heavy atom count {heavy_atoms} exceeds maximum {max_atoms}"
                        )));
                    }
                }
                _ => {
                    // Other constraints would be validated here
                }
            }
        }

        Ok(())
    }

    fn to_qubo(&self) -> ApplicationResult<(QuboModel, HashMap<String, usize>)> {
        // Convert Ising model to QUBO
        let (ising, variable_map) = self.to_ising_model()?;
        let qubo = ising.to_qubo();
        Ok((qubo, variable_map))
    }

    fn evaluate_solution(
        &self,
        solution: &Self::Solution,
    ) -> ApplicationResult<Self::ObjectiveValue> {
        let mut total_score = 0.0;

        // Evaluate based on objectives
        for objective in &self.objectives {
            match objective {
                DrugOptimizationObjective::MaximizeAffinity => {
                    // Simplified affinity score based on molecular properties
                    total_score += solution.drug_likeness_score() * 10.0;
                }
                DrugOptimizationObjective::MaximizeDrugLikeness => {
                    total_score += solution.drug_likeness_score() * 5.0;
                }
                DrugOptimizationObjective::MinimizeToxicity => {
                    // Penalize potentially toxic elements
                    let toxic_penalty = solution
                        .atoms
                        .iter()
                        .filter(|atom| {
                            matches!(atom.atom_type, AtomType::Bromine | AtomType::Iodine)
                        })
                        .count() as f64;
                    total_score -= toxic_penalty * 2.0;
                }
                _ => {
                    total_score += 1.0; // Default score
                }
            }
        }

        Ok(total_score)
    }

    fn is_feasible(&self, solution: &Self::Solution) -> bool {
        // Check basic feasibility constraints
        for constraint in &self.constraints {
            match constraint {
                MolecularConstraint::MolecularWeightRange { min, max } => {
                    let mw = solution.molecular_weight();
                    if mw < *min || mw > *max {
                        return false;
                    }
                }
                MolecularConstraint::LogPRange { min, max } => {
                    let logp = solution.logp_approximation();
                    if logp < *min || logp > *max {
                        return false;
                    }
                }
                MolecularConstraint::MaxHeavyAtoms(max_atoms) => {
                    let heavy_atoms = solution
                        .atoms
                        .iter()
                        .filter(|atom| atom.atom_type != AtomType::Hydrogen)
                        .count();
                    if heavy_atoms > *max_atoms {
                        return false;
                    }
                }
                _ => {
                    // Other constraints
                }
            }
        }

        true
    }
}

impl IndustrySolution for Molecule {
    type Problem = DrugDiscoveryProblem;

    fn from_binary(problem: &Self::Problem, binary_solution: &[i8]) -> ApplicationResult<Self> {
        let solution_i32: Vec<i32> = binary_solution.iter().map(|&x| i32::from(x)).collect();
        let variable_map = HashMap::new(); // Simplified
        problem.solution_to_molecule(&solution_i32, &variable_map)
    }

    fn summary(&self) -> HashMap<String, String> {
        let mut summary = HashMap::new();
        summary.insert("molecule_id".to_string(), self.id.clone());
        summary.insert("num_atoms".to_string(), self.atoms.len().to_string());
        summary.insert("num_bonds".to_string(), self.bonds.len().to_string());
        summary.insert(
            "molecular_weight".to_string(),
            format!("{:.2}", self.molecular_weight()),
        );
        summary.insert(
            "logp".to_string(),
            format!("{:.2}", self.logp_approximation()),
        );
        summary.insert(
            "tpsa".to_string(),
            format!("{:.2}", self.tpsa_approximation()),
        );
        summary.insert(
            "rotatable_bonds".to_string(),
            self.rotatable_bonds().to_string(),
        );
        summary.insert(
            "drug_likeness".to_string(),
            format!("{:.3}", self.drug_likeness_score()),
        );

        if let Some(ref smiles) = self.smiles {
            summary.insert("smiles".to_string(), smiles.clone());
        }

        summary
    }

    fn metrics(&self) -> HashMap<String, f64> {
        let mut metrics = HashMap::new();
        metrics.insert("molecular_weight".to_string(), self.molecular_weight());
        metrics.insert("logp".to_string(), self.logp_approximation());
        metrics.insert("tpsa".to_string(), self.tpsa_approximation());
        metrics.insert("rotatable_bonds".to_string(), self.rotatable_bonds() as f64);
        metrics.insert(
            "drug_likeness_score".to_string(),
            self.drug_likeness_score(),
        );

        let heavy_atom_count = self
            .atoms
            .iter()
            .filter(|atom| atom.atom_type != AtomType::Hydrogen)
            .count() as f64;
        metrics.insert("heavy_atom_count".to_string(), heavy_atom_count);

        // Add any stored properties
        for (key, value) in &self.properties {
            metrics.insert(key.clone(), *value);
        }

        metrics
    }

    fn export_format(&self) -> ApplicationResult<String> {
        let mut output = String::new();

        output.push_str("# Drug Discovery Molecular Result\n");
        writeln!(output, "Molecule ID: {}", self.id).expect("Writing to String should not fail");
        write!(
            output,
            "Molecular Weight: {:.2} Da\n",
            self.molecular_weight()
        )
        .expect("Writing to String should not fail");
        write!(
            output,
            "LogP (Lipophilicity): {:.2}\n",
            self.logp_approximation()
        )
        .expect("Writing to String should not fail");
        write!(output, "TPSA: {:.2} Ų\n", self.tpsa_approximation())
            .expect("Writing to String should not fail");
        write!(output, "Rotatable Bonds: {}\n", self.rotatable_bonds())
            .expect("Writing to String should not fail");
        write!(
            output,
            "Drug-likeness Score: {:.3}\n",
            self.drug_likeness_score()
        )
        .expect("Writing to String should not fail");

        if let Some(ref smiles) = self.smiles {
            writeln!(output, "SMILES: {smiles}").expect("Writing to String should not fail");
        }

        output.push_str("\n# Atoms\n");
        for (i, atom) in self.atoms.iter().enumerate() {
            write!(
                output,
                "Atom {}: {} ({})",
                i,
                atom.atom_type.symbol(),
                atom.atom_type.atomic_number()
            )
            .expect("Writing to String should not fail");
            if atom.formal_charge != 0 {
                write!(output, " charge={}", atom.formal_charge)
                    .expect("Writing to String should not fail");
            }
            if atom.aromatic {
                output.push_str(" aromatic");
            }
            if let Some(coords) = atom.coordinates {
                write!(
                    output,
                    " coords=({:.3}, {:.3}, {:.3})",
                    coords[0], coords[1], coords[2]
                )
                .expect("Writing to String should not fail");
            }
            output.push('\n');
        }

        output.push_str("\n# Bonds\n");
        for (i, bond) in self.bonds.iter().enumerate() {
            write!(
                output,
                "Bond {}: {} - {} ({:?})",
                i, bond.atom1, bond.atom2, bond.bond_type
            )
            .expect("Writing to String should not fail");
            if bond.aromatic {
                output.push_str(" aromatic");
            }
            if bond.in_ring {
                output.push_str(" in_ring");
            }
            output.push('\n');
        }

        if !self.properties.is_empty() {
            output.push_str("\n# Properties\n");
            for (key, value) in &self.properties {
                writeln!(output, "{key}: {value:.6}").expect("Writing to String should not fail");
            }
        }

        Ok(output)
    }
}

/// Create benchmark drug discovery problems
pub fn create_benchmark_problems(
    size: usize,
) -> ApplicationResult<Vec<Box<dyn OptimizationProblem<Solution = Molecule, ObjectiveValue = f64>>>>
{
    let mut problems = Vec::new();

    // Create molecules of different complexity
    let molecule_specs = match size {
        s if s <= 10 => {
            vec![
                (
                    "aspirin",
                    vec![
                        (AtomType::Carbon, 9),
                        (AtomType::Hydrogen, 8),
                        (AtomType::Oxygen, 4),
                    ],
                ),
                (
                    "caffeine",
                    vec![
                        (AtomType::Carbon, 8),
                        (AtomType::Hydrogen, 10),
                        (AtomType::Nitrogen, 4),
                        (AtomType::Oxygen, 2),
                    ],
                ),
            ]
        }
        s if s <= 25 => {
            vec![
                (
                    "ibuprofen",
                    vec![
                        (AtomType::Carbon, 13),
                        (AtomType::Hydrogen, 18),
                        (AtomType::Oxygen, 2),
                    ],
                ),
                (
                    "acetaminophen",
                    vec![
                        (AtomType::Carbon, 8),
                        (AtomType::Hydrogen, 9),
                        (AtomType::Nitrogen, 1),
                        (AtomType::Oxygen, 2),
                    ],
                ),
                (
                    "penicillin",
                    vec![
                        (AtomType::Carbon, 16),
                        (AtomType::Hydrogen, 18),
                        (AtomType::Nitrogen, 2),
                        (AtomType::Oxygen, 4),
                        (AtomType::Sulfur, 1),
                    ],
                ),
            ]
        }
        _ => {
            vec![
                (
                    "vancomycin",
                    vec![
                        (AtomType::Carbon, 66),
                        (AtomType::Hydrogen, 75),
                        (AtomType::Chlorine, 2),
                        (AtomType::Nitrogen, 9),
                        (AtomType::Oxygen, 24),
                    ],
                ),
                (
                    "doxorubicin",
                    vec![
                        (AtomType::Carbon, 27),
                        (AtomType::Hydrogen, 29),
                        (AtomType::Nitrogen, 1),
                        (AtomType::Oxygen, 11),
                    ],
                ),
            ]
        }
    };

    for (i, (name, atom_composition)) in molecule_specs.iter().enumerate() {
        let mut molecule = Molecule::new(format!("benchmark_{name}"));

        // Add atoms based on composition
        let mut atom_id = 0;
        for (atom_type, count) in atom_composition {
            for _ in 0..*count {
                let atom = Atom::new(atom_id, *atom_type);
                molecule.add_atom(atom);
                atom_id += 1;
            }
        }

        // Add some basic bonds (simplified)
        for j in 0..(molecule.atoms.len() - 1) {
            let bond = Bond::new(j, j + 1, BondType::Single);
            molecule.add_bond(bond);
        }

        // Create drug-target interaction
        let target_interaction = DrugTargetInteraction {
            drug_molecule: molecule,
            target_id: format!("target_{i}"),
            target_type: TargetType::Enzyme,
            binding_affinity: Some(6.5), // Example pIC50
            selectivity: HashMap::new(),
            admet_properties: AdmetProperties::default(),
        };

        let mut problem = DrugDiscoveryProblem::new(target_interaction);

        // Add different objectives for different problems
        match i % 3 {
            0 => problem = problem.add_objective(DrugOptimizationObjective::MaximizeAffinity),
            1 => problem = problem.add_objective(DrugOptimizationObjective::MaximizeDrugLikeness),
            2 => problem = problem.add_objective(DrugOptimizationObjective::MinimizeToxicity),
            _ => problem = problem.add_objective(DrugOptimizationObjective::OptimizeAdmet),
        }

        // Add molecular constraints
        problem = problem.add_constraint(MolecularConstraint::MolecularWeightRange {
            min: 100.0,
            max: 800.0,
        });
        problem = problem.add_constraint(MolecularConstraint::LogPRange {
            min: -2.0,
            max: 5.0,
        });

        // Note: Simplified for trait object compatibility
        problems.push(Box::new(problem)
            as Box<
                dyn OptimizationProblem<Solution = Molecule, ObjectiveValue = f64>,
            >);
    }

    Ok(problems)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_atom_creation() {
        let atom = Atom::new(0, AtomType::Carbon)
            .with_charge(-1)
            .with_coordinates([1.0, 2.0, 3.0])
            .set_aromatic(true);

        assert_eq!(atom.id, 0);
        assert_eq!(atom.atom_type, AtomType::Carbon);
        assert_eq!(atom.formal_charge, -1);
        assert_eq!(atom.coordinates, Some([1.0, 2.0, 3.0]));
        assert!(atom.aromatic);
    }

    #[test]
    fn test_molecule_properties() {
        let mut molecule = Molecule::new("test".to_string());

        // Add carbon and hydrogen atoms
        molecule.add_atom(Atom::new(0, AtomType::Carbon));
        molecule.add_atom(Atom::new(1, AtomType::Hydrogen));
        molecule.add_atom(Atom::new(2, AtomType::Oxygen));

        assert_eq!(molecule.atoms.len(), 3);
        assert!(molecule.molecular_weight() > 0.0);

        let drug_likeness = molecule.drug_likeness_score();
        assert!(drug_likeness > 0.0 && drug_likeness <= 1.0);
    }

    #[test]
    fn test_atom_type_properties() {
        assert_eq!(AtomType::Carbon.atomic_number(), 6);
        assert_eq!(AtomType::Carbon.symbol(), "C");
        assert_eq!(AtomType::Carbon.valence(), 4);

        assert_eq!(AtomType::Nitrogen.atomic_number(), 7);
        assert_eq!(AtomType::Oxygen.valence(), 2);
    }

    #[test]
    fn test_bond_properties() {
        let bond = Bond::new(0, 1, BondType::Double)
            .set_aromatic(true)
            .set_in_ring(true);

        assert_eq!(bond.atom1, 0);
        assert_eq!(bond.atom2, 1);
        assert_eq!(bond.bond_type, BondType::Double);
        assert_eq!(bond.bond_type.bond_order(), 2.0);
        assert!(bond.aromatic);
        assert!(bond.in_ring);
    }

    #[test]
    fn test_molecular_calculations() {
        let mut molecule = Molecule::new("ethanol".to_string());

        // Create ethanol: C2H6O
        molecule.add_atom(Atom::new(0, AtomType::Carbon));
        molecule.add_atom(Atom::new(1, AtomType::Carbon));
        molecule.add_atom(Atom::new(2, AtomType::Oxygen));
        for i in 3..9 {
            molecule.add_atom(Atom::new(i, AtomType::Hydrogen));
        }

        // Add bonds
        molecule.add_bond(Bond::new(0, 1, BondType::Single));
        molecule.add_bond(Bond::new(1, 2, BondType::Single));

        let mw = molecule.molecular_weight();
        assert!(mw > 40.0 && mw < 50.0); // Approximately 46 Da

        let logp = molecule.logp_approximation();
        assert!(logp > -2.0 && logp < 2.0); // Reasonable LogP for ethanol
    }

    #[test]
    fn test_drug_discovery_problem() {
        let mut molecule = Molecule::new("test_drug".to_string());
        molecule.add_atom(Atom::new(0, AtomType::Carbon));
        molecule.add_atom(Atom::new(1, AtomType::Nitrogen));

        let target_interaction = DrugTargetInteraction {
            drug_molecule: molecule,
            target_id: "test_target".to_string(),
            target_type: TargetType::Enzyme,
            binding_affinity: Some(7.0),
            selectivity: HashMap::new(),
            admet_properties: AdmetProperties::default(),
        };

        let problem = DrugDiscoveryProblem::new(target_interaction)
            .add_objective(DrugOptimizationObjective::MaximizeAffinity)
            .add_constraint(MolecularConstraint::MolecularWeightRange {
                min: 20.0,
                max: 500.0,
            });

        assert!(problem.validate().is_ok());

        let metrics = problem.size_metrics();
        assert_eq!(metrics["num_atoms"], 2);
    }

    #[test]
    fn test_ising_conversion() {
        let mut molecule = Molecule::new("test".to_string());
        molecule.add_atom(Atom::new(0, AtomType::Carbon));
        molecule.add_atom(Atom::new(1, AtomType::Nitrogen));

        let target_interaction = DrugTargetInteraction {
            drug_molecule: molecule,
            target_id: "test".to_string(),
            target_type: TargetType::Enzyme,
            binding_affinity: None,
            selectivity: HashMap::new(),
            admet_properties: AdmetProperties::default(),
        };

        let problem = DrugDiscoveryProblem::new(target_interaction);
        let (ising, variable_map) = problem
            .to_ising_model()
            .expect("should convert to Ising model");

        assert_eq!(ising.num_qubits, 16); // 2 atoms * 8 bits each
        assert!(!variable_map.is_empty());
    }
}