Advanced AI-powered nanoformulation design platform for pharmaceutical development. Design, optimize, and validate nanocarrier systems with cutting-edge computational tools for drug delivery research and development.
Advanced machine learning algorithms for optimization
Statistical design of experiments for robust formulations
Predictive models for formulation optimization
Automated pharmaceutical-grade protocol creation
Input your active pharmaceutical ingredient (API) using chemical name, SMILES, or InChI. AI resolves molecular properties automatically.
Choose optimal nanoformulation technique based on AI scoring and molecular properties analysis.
Generate statistically robust experimental designs using advanced DoE methodologies.
Use Bayesian optimization to find optimal conditions and generate manufacturing protocols.
Academic & Commercial Use: This platform incorporates advanced AI and computational methods. Users should cite appropriate sources and respect intellectual property rights. For commercial applications, ensure compliance with relevant licensing requirements.
By using this platform, you acknowledge and agree to these terms and understand the research-only nature of this tool.
Generate experimental design space for your selected technique.
Optimize your formulation using advanced Bayesian methods.
NanoCraft AI is a comprehensive, open-source platform designed to accelerate nanocarrier development through AI-driven computational techniques. The platform integrates physicochemical analysis, formulation technique scoring, design of experiments (DoE), machine learning modeling, and Bayesian optimization to guide pharmaceutical scientists from compound characterization to optimized nanoformulation protocols.
Principle: Controlled precipitation of polymer-drug conjugates through solvent displacement in a non-solvent environment.
Mechanism: Drug and polymer dissolved in water-miscible organic solvent → Rapid injection into aqueous antisolvent → Instantaneous nucleation via Marangoni effect → Particle growth controlled by Ostwald ripening inhibition.
Optimal Applications: Hydrophobic drugs (log P > 2), BCS Class II/IV compounds, Polymer MW: 5,000-50,000 Da, Drug loading: 5-40% w/w, Particle size: 50-300 nm
Critical Parameters: Organic:aqueous ratio (1:2 to 1:10), Injection rate (0.5-10 mL/min), Stirring speed (200-1500 rpm), Temperature (15-40°C)
Principle: Spontaneous lipid self-assembly through rapid ethanol dilution below critical aggregation concentration.
Mechanism: Phospholipids dissolved in ethanol → Rapid injection creates local supersaturation → Lipid monomers aggregate into bilayer sheets → Vesicle closure forms unilamellar liposomes.
Optimal Applications: Both hydrophilic (core) and hydrophobic (bilayer) drugs, pH-sensitive formulations, Drug loading: 5-30% for hydrophilic, up to 50% for hydrophobic
Critical Parameters: Ethanol:water ratio (1:3 to 1:20), Lipid concentration (1-50 mg/mL), Temperature (25-65°C, above Tm), Cholesterol content (0-50 mol%)
Principle: Ionizable lipid-based nanostructures for nucleic acid delivery with pH-responsive endosomal escape.
Mechanism: Microfluidic mixing induces lipid self-assembly → Ionizable lipids complex with nucleic acids at acidic pH → pH-triggered membrane fusion enables cytoplasmic delivery.
Optimal Applications: mRNA vaccines and therapeutics, siRNA/miRNA delivery, Gene editing cargo (CRISPR), Particle size: 60-120 nm, Encapsulation efficiency: 80-95%
Critical Parameters: Ionizable lipid pKa (6.0-6.8), N/P ratio (3-6), Flow rate ratio (3:1 to 12:1), Mixing pH (3.5-4.5)
Principle: Controlled nanoprecipitation through precise fluid manipulation in microchannels with predictable mixing dynamics.
Mechanism: Chaotic advection in herringbone mixers → Diffusion-dominated mixing at low Reynolds numbers → Rapid solvent exchange (milliseconds) → Uniform supersaturation prevents polydispersity.
Optimal Applications: LNPs and liposomes, Polymeric nanoparticles, Scalable GMP manufacturing, Size control: 20-500 nm
Critical Parameters: Flow rate ratio (1:1 to 20:1), Total flow rate (0.5-20 mL/min), Residence time (10-1000 ms), Temperature control (±0.5°C)
Principle: Drug incorporation in solid lipid matrix providing controlled release and improved stability.
Mechanism: Hot homogenization disperses molten lipid phase → High-pressure creates nanoemulsion → Controlled cooling crystallizes lipid core → Drug trapped in lipid crystal lattice.
Optimal Applications: Lipophilic drugs (log P 2-7), Oral bioavailability enhancement, Sustained/controlled release, Brain targeting, Particle size: 50-1000 nm
Critical Parameters: Homogenization pressure (100-2000 bar), Temperature (5-10°C above lipid Tm), Surfactant concentration (0.5-5%), Number of cycles (3-10)
Principle: Spontaneous emulsification through phase transitions without external energy input.
Mechanism: Phase inversion temperature (PIT) method → Ultralow interfacial tension (<10⁻³ mN/m) → Thermodynamically driven droplet formation.
Optimal Applications: Lipophilic drugs (log P > 3), Oral bioavailability enhancement, Topical/transdermal delivery, Droplet size: 20-200 nm
Critical Parameters: HLB value optimization (8-18), PIT determination (40-90°C), Oil:surfactant:water ratio, Cooling/dilution rate
Principle: Rapid solvent evaporation from atomized droplets producing solid particles with controlled morphology.
Mechanism: Two-fluid or rotary atomization → Droplet drying in hot gas stream → Surface enrichment and crust formation → Cyclone separation and collection.
Optimal Applications: Inhalation formulations (1-5 μm), Solid dispersions for oral delivery, Protein/peptide stabilization, Particle size: 0.1-100 μm
Critical Parameters: Inlet temperature (100-220°C), Outlet temperature (40-100°C), Feed rate (2-50 mL/min), Atomization pressure/speed
Principle: Amphiphilic block copolymer self-assembly above critical micelle concentration forming core-shell nanostructures.
Mechanism: Entropy-driven hydrophobic aggregation → Core-shell architecture formation → Drug solubilization in hydrophobic core → Steric stabilization by hydrophilic corona.
Optimal Applications: Poorly soluble drugs (log P > 2), Tumor targeting via EPR effect, Stimuli-responsive drug release, Size range: 10-100 nm
Critical Parameters: CMC (10⁻⁷ to 10⁻⁴ M), Drug:polymer ratio (0.05-0.5 w/w), Temperature (20-60°C), Ionic strength effects
Principle: Monodisperse, hyperbranched macromolecules with defined architecture for precise drug conjugation/encapsulation.
Mechanism: Generation-dependent size growth → Interior cavities for drug encapsulation → Surface groups for conjugation → pH-responsive protonation behavior.
Optimal Applications: Small molecule drugs (<1000 Da), Gene delivery (polycationic dendrimers), Targeted therapy, Imaging agents, Size range: 2-15 nm
Critical Parameters: Generation number (G0-G10), Core type and functionality, Surface group modification, Drug:dendrimer ratio, pH optimization (6.5-7.8)
Principle: Top-down particle size reduction through mechanical attrition in liquid medium with stabilizers.
Mechanism: Impact and shear forces fracture crystals → Stabilizer adsorption prevents aggregation → Ostwald ripening inhibition → Size reduction follows first-order kinetics.
Optimal Applications: BCS Class II/IV drugs, Immediate release formulations, Injectable nanosuspensions, Ophthalmic delivery, Size range: 100-2000 nm
Critical Parameters: Milling media size (0.1-2 mm), Drug concentration (5-40% w/w), Milling speed (200-1500 rpm), Milling time (0.5-72 hours)
Principle: Exploiting unique properties of supercritical CO₂ for particle formation and drug encapsulation.
Mechanism: Rapid expansion (RESS) induces nucleation → Anti-solvent precipitation (SAS/GAS) → Tunable solvent properties → Solvent-free processing.
Optimal Applications: Thermolabile compounds, Inhalation particles, Controlled morphology, Organic solvent-free processing, Size range: 50-5000 nm
Critical Parameters: Pressure (75-400 bar), Temperature (31-80°C), CO₂ flow rate, Nozzle geometry, Co-solvent addition (0-20%)
Principle: Electrostatic atomization of polymer solutions forming mono-disperse particles through coulombic fission.
Mechanism: Taylor cone formation at needle tip → Jet breakup via varicose instability → Coulombic fission creates droplets → Solvent evaporation during flight.
Optimal Applications: Protein/peptide encapsulation, Core-shell particles, Living cell encapsulation, Controlled release matrices, Size range: 100 nm - 100 μm
Critical Parameters: Applied voltage (5-30 kV), Flow rate (0.01-10 mL/h), Needle-collector distance (5-30 cm), Solution conductivity, Polymer concentration (1-30% w/v)
Principle: Sequential adsorption of oppositely charged polyelectrolytes building multilayer films on template particles.
Mechanism: Electrostatic attraction drives adsorption → Charge overcompensation enables alternation → Interpenetration creates stable layers → Template dissolution yields hollow capsules.
Optimal Applications: Multi-drug delivery systems, Stimuli-responsive release, Surface functionalization, Enzyme immobilization, Size range: 50 nm - 10 μm
Critical Parameters: Polyelectrolyte MW and charge density, pH (affects ionization), Ionic strength (0.01-1 M), Deposition time (1-30 min), Number of layers (2-50)
Principle: Spontaneous emulsification of lipid formulations upon aqueous dilution forming drug-loaded nanoemulsions.
Mechanism: Surfactant migration to oil-water interface → Rapid dispersion without external energy → Fine emulsion formation (SMEDDS <100 nm) → Enhanced drug solubilization.
Optimal Applications: BCS Class II drugs (optimal), BCS Class IV drugs (good), Oral bioavailability enhancement, Reduced food effect, First-pass metabolism bypass
Critical Parameters: Oil selection (log P 2-7), Surfactant HLB (12-18), Co-surfactant ratio, Drug loading (5-30% w/w), Dilution ratio (1:50 to 1:1000)
Principle: Rapid solvent exchange triggers amphiphile aggregation into thermodynamically stable micelles.
Mechanism: Solvent miscibility creates supersaturation → Nucleation above CMC → Growth via monomer addition → Equilibration to optimal aggregation number.
Optimal Applications: Hydrophobic drug solubilization, Injectable formulations, Ophthalmic delivery, Small molecule encapsulation, Size range: 5-100 nm
Critical Parameters: CMC determination, Solvent selection (DMSO, DMF, ethanol), Mixing rate and method, Final solvent content (<5%), Temperature effects on CMC
NanoCraft AI employs a sophisticated scoring algorithm evaluating techniques across multiple weighted criteria:
Log P Matching:
Score = exp(-0.5 × ((log P - optimal_log P) / σ)²)
Optimal log P values by technique:
Process Scalability Scores:
Route-Specific Multipliers:
Stability Ranking (1.0 = most stable):
Time-to-Clinic Estimates:
final_score = base_score × (1 + Σ(objective_weight_i × compatibility_i))
Objective modifiers:
Application: Complete exploration with ≤4 factors
Structure:
Runs = 2^k × replicates + center_pointsPower analysis: α = 0.05, β = 0.20, effect size = 2σ
Example for LNP optimization (2³ design):
Resolution Selection:
Generator Selection:
2^(7-2) Resolution IV: I = ABCEF = BCDEGRotatability and Orthogonality:
α = (2^k)^(1/4) for rotatabilityα = √k for orthogonal blocking
Face-Centered CCD for constrained spaces:
Advantages:
Application Example - Liposome Optimization:
D-Optimal Criterion:
Maximize |X'X| → Minimize average prediction varianceG-efficiency = (p/n) × 100 / max(leverage)
I-Optimal for Prediction:
Minimize ∫(variance of ŷ)dχ over design spaceHierarchical Model Building:
1. Linear model: Y = β₀ + Σβᵢxᵢ2. Add interactions: + ΣΣβᵢⱼxᵢxⱼ3. Add quadratic: + Σβᵢᵢxᵢ²
Model Diagnostics:
Desirability Function:
Individual: dᵢ = [(yᵢ - Lᵢ)/(Tᵢ - Lᵢ)]^rOverall: D = (∏dᵢ^wᵢ)^(1/Σwᵢ)
Pareto Optimization:
Regularization Balance:
Loss = MSE + α[ρ||β||₁ + (1-ρ)||β||₂²/2]Advantages:
Kernel Selection for Formulation:
Matérn 5/2: k(x,x') = σ²(1 + √5r + 5r²/3)exp(-√5r)r = ||x - x'||/ℓ
Hyperparameter Optimization:
Formulation-Specific Tuning:
XGBoost Parameters:
Physicochemical Descriptors:
Process Parameters:
Cross-Validation Strategies:
RMSE: √(Σ(yᵢ - ŷᵢ)²/n)MAE: Σ|yᵢ - ŷᵢ|/nMAPE: 100 × Σ|yᵢ - ŷᵢ|/yᵢ/nR²: 1 - SS_res/SS_totQ²: Cross-validated R²
EI(x) = (μ(x) - f⁺ - ξ)Φ(Z) + σ(x)φ(Z)Z = (μ(x) - f⁺ - ξ)/σ(x)KG(x) = E[max_x' μⁿ⁺¹(x') | xⁿ⁺¹ = x] - max_x' μⁿ(x')
Probabilistic Constraints:
P(g(x) ≤ 0) = Φ(-μ_g(x)/σ_g(x))EIC(x) = EI(x) × ∏P(gᵢ(x) ≤ 0)
Examples:
Hypervolume Improvement:
HVI = ∫_S I(y)p(y|x)dyScalarization Methods:
Nanoparticle Systems:
Risk Assessment Matrix:
RPN = Severity × Occurrence × DetectionIn-Line Measurements:
u(t) = Kp×e(t) + Ki×∫e(t)dt + Kd×de/dtUse the main navigation tabs to access specific workflow sections. Each section contains detailed tools and explanations for compound analysis, technique scoring, experimental design, and protocol generation. The platform guides you through the complete nanoformulation development process from initial compound characterization to optimized manufacturing protocols.
Application: Complete exploration with ≤4 factors
Structure:
Runs = 2^k × replicates + center_pointsPower analysis: α = 0.05, β = 0.20, effect size = 2σ
Resolution Selection:
Rotatability and Orthogonality:
α = (2^k)^(1/4) for rotatabilityα = √k for orthogonal blocking
Advantages:
D-Optimal Criterion:
Maximize |X'X| → Minimize average prediction varianceG-efficiency = (p/n) × 100 / max(leverage)
I-Optimal for Prediction:
Minimize ∫(variance of ŷ)dχ over design space
Hierarchical Model Building:
Linear model: Y = β₀ + ΣβᵢxᵢAdd interactions: + ΣΣβᵢⱼxᵢxⱼAdd quadratic: + Σβᵢᵢxᵢ²
Desirability Function:
Individual: dᵢ = [(yᵢ - Lᵢ)/(Tᵢ - Lᵢ)]^rOverall: D = (∏dᵢ^wᵢ)^(1/Σwᵢ)
Regularization Balance:
Loss = MSE + α[ρ||β||₁ + (1-ρ)||β||₂²/2]Kernel Selection for Formulation:
Matérn 5/2: k(x,x') = σ²(1 + √5r + 5r²/3)exp(-√5r)r = ||x - x'||/ℓ
Formulation-Specific Tuning:
Physicochemical Descriptors:
Process Parameters:
RMSE: √(Σ(yᵢ - ŷᵢ)²/n)MAE: Σ|yᵢ - ŷᵢ|/nR²: 1 - SS_res/SS_totQ²: Cross-validated R²
EI(x) = (μ(x) - f⁺ - ξ)Φ(Z) + σ(x)φ(Z)Z = (μ(x) - f⁺ - ξ)/σ(x)KG(x) = E[max_x' μⁿ⁺¹(x') | xⁿ⁺¹ = x] - max_x' μⁿ(x')
Probabilistic Constraints:
P(g(x) ≤ 0) = Φ(-μ_g(x)/σ_g(x))EIC(x) = EI(x) × ∏P(gᵢ(x) ≤ 0)
Examples:
HVI = ∫_S I(y)p(y|x)dyNanoparticle Systems:
Risk Assessment Matrix:
RPN = Severity × Occurrence × DetectionIn-Line Measurements:
u(t) = Kp×e(t) + Ki×∫e(t)dt + Kd×de/dtUse the main navigation tabs to access specific workflow sections. Each section contains detailed tools and explanations for compound analysis, technique scoring, experimental design, and protocol generation. The platform guides you through the complete nanoformulation development process from initial compound characterization to optimized manufacturing protocols.