AI-Driven Optimization of Nanoparticle Synthesis Parameters for Tailored Biomedical Applications

Amara L. Vaziri; Kenji H. Morimoto; Liora S. Nadeem

AI-Driven Optimization of Nanoparticle Synthesis Parameters for Tailored Biomedical Applications

Authors: Amara L. Vaziri, Kenji H. Morimoto, Liora S. Nadeem

Journal: International Journal of Nanotechnology and Engineering Applications (IJNEA), ISSN 3023-3747

Citation: IJNEA 6(1), 2026-05-10.

Type: Original Research

Abstract

Background: Tailoring nanoparticle properties (size, surface charge, polydispersity, yield, and biological performance) to specific biomedical applications remains time-consuming and resource-intensive. Recent studies have demonstrated promise for AI and metaheuristic methods to accelerate experimental design and optimize synthesis conditions. Methods: We assembled a multi-protocol experimental dataset (n = 420 synthesis runs) spanning chemical, green, and enzyme-mediated routes, measuring physicochemical outputs (size, PDI, zeta potential, yield) and application-specific bioendpoints (in vitro cell viability, antibacterial efficacy). Surrogate models (Random Forest, Gaussian Process Regression for Bayesian Optimization, and feedforward ANN) were trained with nested cross-validation. Metaheuristic search (genetic algorithm) and Bayesian optimization were applied to identify Pareto-optimal synthesis recipes for two target profiles: drug-delivery nanoparticles (small size, low PDI, high biocompatibility) and antibacterial nanoparticles (small size, high surface charge magnitude, high antimicrobial activity). Model interpretability utilized SHAP and sensitivity analysis. Results: AI-driven surrogate models achieved high predictive performance (best R2 on hold-out test: Random Forest R2 = 0.86 for particle size; Gaussian Process R2 = 0.83 for zeta potential). Optimization produced synthesis parameter sets that improved targeted metrics by 18–42% relative to baseline DOE-optimized recipes. Feature importance and SHAP analysis identified precursor concentration, pH, capping agent ratio, and reaction temperature as primary levers across endpoints. Tables and figures summarize model performance, regression coefficients/importance rankings, and optimized parameter sets. Conclusions: The integrated AI + metaheuristic framework substantially reduces experimental search space and yields application-specific synthesis protocols with demonstrable gains in physicochemical and biological performance. The approach is generalizable across nanoparticle classes and supports rapid translation to tailored biomedical use cases. Future work should expand datasets, integrate active learning with closed-loop experimentation, and validate optimized recipes in relevant in vivo models.

Keywords

nanoparticle synthesis, AI-driven optimization, Bayesian optimization, genetic algorithm, biomedical applications, surrogate modeling, explainable AI

Full Text

<h2>Introduction</h2>Nanoparticles (NPs) are central to a broad range of biomedical technologies including drug delivery, imaging, biosensing, and antimicrobial therapies. The functional performance of a nanoparticle is governed by a combination of intrinsic physicochemical properties such as primary particle size, size distribution (polydispersity index, PDI), surface chemistry and zeta potential, as well as yield and colloidal stability (Dong et al., 2016; Chavali & Nikolova, 2019). Achieving a specific target profile for a given biomedical application requires careful control of synthesis variables (precursor concentration, reducing agent ratio, pH, temperature, capping agent, reaction time) and often multiple rounds of empirical optimization (Unsoy et al., 2012; Indira, 2015).Traditional design of experiments (DOE) and response surface methodology (RSM) can be effective but scale poorly when the number of controllable factors increases or when multiple, sometimes competing, objectives must be optimized simultaneously (Putra et al., 2025; Shukla & Bandyopadhyay, 2023). AI-driven surrogate modeling and metaheuristic optimization techniques offer a promising alternative: they can learn complex, nonlinear mappings from synthesis parameters to performance metrics and then identify Pareto-optimal parameter sets that satisfy application-specific constraints (Kapoor et al., 2024). Recent case studies have applied machine learning to nanoparticle synthesis for environmental remediation and antibacterial optimization (Putra et al., 2025; Khan et al., 2026), and AI-driven design frameworks for nanoparticle-based drug delivery have been proposed to streamline formulation development (Kapoor et al., 2024).The present study develops and validates an integrated AI-driven optimization pipeline that combines surrogate models (Random Forest, Gaussian Process Regression (GPR), and feedforward artificial neural networks (ANN)) with metaheuristic and Bayesian optimization to derive synthesis parameter recommendations tailored to two prototypical biomedical use cases: (1) drug-delivery NPs requiring small size (<100 nm), low PDI, neutral-to-moderate surface charge, and high biocompatibility; and (2) antimicrobial NPs (silver and copper-based) requiring small size (10–50 nm), high absolute zeta potential magnitude, and high bactericidal activity. We assembled a broad experimental dataset covering chemical, green, and enzyme-mediated syntheses and benchmarked AI-derived protocols against RSM and classical DOE baselines. Interpretability tools (SHAP) were used to elucidate mechanistic parameter–response relationships, thereby increasing trust and facilitating experimental adoption.This paper is organized as follows: a targeted review of relevant literature is presented next, followed by a detailed description of dataset curation, modeling, and optimization methodology. Results include surrogate-model performance, optimized recipes, and explainability outputs. We conclude with a discussion of limitations, translational considerations, and directions for future work.<h2>Literature Review</h2>Optimization of nanoparticle synthesis is a mature field with a long history of chemical and green routes producing diverse nanomaterials for biomedical applications (Unsoy et al., 2012; Rauwel, 2017). Classical approaches emphasize control of nucleation and growth by modulating precursor concentration, temperature, pH, and stabilizing agents (Unsoy et al., 2012; J, 2015). Green synthesis using plant extracts or essential oils has grown in popularity because of lower toxicity and sustainability benefits; however, these approaches add complexity due to biological variability in extract composition (Rauwel, 2017).Beyond bench chemistry, computational approaches for design and optimization have been applied intermittently to nanomaterials. Response surface methodology and factorial DOE remain standard in many laboratories because they are interpretable and relatively easy to implement (Shukla & Bandyopadhyay, 2023). More recently, machine learning (ML) methods have been used to predict NP properties from synthesis conditions and to accelerate screening of formulation space (Kapoor et al., 2024). Specific examples include ML-driven optimization for wastewater remediation using metal nanoparticles (Putra et al., 2025) and optimization of silver nanoparticle synthesis using leaf extracts via RSM (Khan et al., 2026). These studies demonstrate the potential for AI to reduce experimental burden but often target a single application or a limited parameter space.Hybrid frameworks that combine surrogate modeling with metaheuristic or Bayesian optimization are increasingly common in engineering domains where experiments are expensive (Syed, 2025). For nanomaterials, such hybrid strategies can navigate multi-dimensional, multi-objective landscapes more efficiently than grid search or classical DOE. Gaussian Process Regression is frequently used as a surrogate in Bayesian optimization because it provides uncertainty estimates that guide exploration–exploitation trade-offs (Syed, 2025). Random Forests and ANNs are effective when larger datasets are available and when complex nonlinear interactions exist (Shukla & Bandyopadhyay, 2023; Sampath, 2025).Explainable AI (XAI) methods such as SHAP and feature importance ranking are critical to translate AI-derived parameter recommendations into experimental practice. These tools can highlight which synthesis levers most strongly influence a target endpoint and can uncover unexpected dependencies that suggest mechanistic hypotheses (Kapoor et al., 2024; Shukla & Bandyopadhyay, 2023). Complementary to optimization, multi-objective Pareto analysis enables researchers to visualize trade-offs between competing goals (e.g., minimizing size while maximizing biocompatibility) and select compromise solutions consistent with practical constraints.Finally, there is growing recognition of the importance of integrating AI frameworks with closed-loop experimental systems (autonomous laboratories) to enable active learning and real-time updating of models (Sampath, 2025). While fully autonomous workflows remain rare in nanoparticle synthesis, recent engineering work in adjacent fields demonstrates feasibility and highlights requirements for robust surrogate modeling, uncertainty quantification, and safe exploration (Syed, 2025; Yesane, 2024).In sum, the literature supports AI and metaheuristic methods as powerful enablers for optimized nanoparticle synthesis, but systematic comparative studies that evaluate multiple surrogate models, interpretability approaches, and practical optimization strategies for diverse biomedical targets remain limited. This study addresses this gap by evaluating an integrated pipeline across multiple synthesis modalities and application-defined objective sets.<h2>Methodology</h2>Overview. The methodological workflow comprises dataset assembly and preprocessing, surrogate model training and validation, multi-objective optimization using genetic algorithms (GA) and Bayesian optimization (BO) with Gaussian Process surrogates, and interpretability analysis using SHAP. The pipeline was implemented in Python 3.9 with scikit-learn for classical models, GPyTorch for Gaussian Process Regression, a lightweight ANN implemented in PyTorch, and DEAP for genetic algorithm search. Design and evaluation emphasize reproducibility via fixed random seeds and nested cross-validation.Experimental dataset. We collated data from 420 independent synthesis experiments carried out in-house or curated from high-quality open experimental records spanning chemical reduction routes, plant-extract-mediated green syntheses, and enzyme-mediated methods. Experiments were recorded with seven continuous input variables: precursor concentration (mM), reducing agent ratio (molar ratio), pH, temperature (°C), reaction time (min), stirring speed (rpm), and capping agent (% w/v). Categorical inputs (synthesis route type: chemical, green, enzyme) were one-hot encoded. Measured outputs included: hydrodynamic diameter (nm) by DLS, PDI, zeta potential (mV), mass yield (%), in vitro cell viability at 24 h (percentage of control using MTT assay), and antibacterial efficacy (minimum inhibitory concentration, MIC, µg/mL) for silver- and copper-based NPs. Data ranges and summary statistics are reported in the Results section (Table 1).Preprocessing. Continuous inputs and outputs were standardized (z-score) for modeling except where physical units were required for interpretability (optimized recipes reported in original units). Outliers were identified using a robust median-absolute-deviation rule and inspected; only 4 experiments (<1%) were removed due to clear recording errors. Missing values were handled by simple imputation using k-nearest neighbors (k = 5) only for non-critical covariates; primary outputs were complete.Surrogate models and training. Three primary surrogate classes were evaluated: Random Forest Regression (RF), Gaussian Process Regression (GPR) with Matérn kernel, and feedforward artificial neural network (ANN) with two hidden layers (64 and 32 units, ReLU activations). Hyperparameters were tuned with nested 5-fold cross-validation and Bayesian hyperparameter search for ANN and GPR lengthscale priors. Model selection prioritized predictive R2, RMSE, and calibration of uncertainty (for GPR). Ensemble RF models (1000 trees) were used with feature subsampling to improve generalization. Performance metrics on a held-out test split (20% stratified by synthesis route) are summarized in Results (Table 2).Optimization targets and constraints. Two target application profiles were defined a priori in consultation with domain experts: (A) Drug-delivery profile: target hydrodynamic diameter < 100 nm, PDI < 0.2, cell viability > 85% at 24 h, yield > 50%; (B) Antibacterial profile (Ag/Cu NPs): target diameter 10–50 nm, MIC < 50 µg/mL, zeta potential magnitude > 30 mV, yield > 40%. The optimization problem is multi-objective with soft constraints; objective scalarization and Pareto front generation were both used.Optimization algorithms. Two complementary optimization strategies were applied: (1) Bayesian optimization (BO) using GPR surrogates with expected hypervolume improvement acquisition function for multi-objective search; and (2) Genetic Algorithm (GA) for global search using surrogate evaluations. For BO, uncertainty estimates from GPR guided exploration; for GA, population size = 200, tournament selection, crossover probability = 0.9, mutation probability = 0.2, and 200 generations. For comparison, classical RSM-derived optima from factorial DOE (second-order polynomial fits) were computed on a subset of the parameter space and used as baseline recipes.Explainability and sensitivity. SHAP (SHapley Additive exPlanations) values were computed for RF and ANN models to quantify feature contributions to predictions. Additional sensitivity analysis was carried out by perturbing individual synthesis parameters around optimized points and measuring predicted response changes. Pareto fronts were visualized to display trade-offs between size, biocompatibility, and antibacterial effectiveness.Validation. A subset of optimized recipes (n = 12, six per target profile) recommended by BO and GA were validated experimentally to confirm predicted property improvements. Validation metrics included DLS size/PDI, zeta potential, yield, in vitro cell viability (MTT), and MIC assays against Escherichia coli and Staphylococcus aureus. Experimental validation results are presented alongside model-based predictions to assess transferability from in silico optimization to bench outcomes.Ethical considerations. Standard biosafety protocols were followed for antimicrobial assays and cytotoxicity testing. No human or animal subjects were involved.<h2>Results</h2>Dataset descriptive statistics. Table 1 summarizes the experimental dataset (n = 420) across primary outputs and illustrates the diversity of measured outcomes. The dataset contains balanced representation across synthesis routes (chemical n = 160, green n = 140, enzyme-mediated n = 120) and covers wide ranges of precursor concentrations and pH values.<table style="min-width: 150px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><th colspan="1" rowspan="1">Variable</th><th colspan="1" rowspan="1">n</th><th colspan="1" rowspan="1">Mean</th><th colspan="1" rowspan="1">SD</th><th colspan="1" rowspan="1">Min</th><th colspan="1" rowspan="1">Max</th></tr><tr><td colspan="1" rowspan="1">Hydrodynamic diameter (nm)</td><td colspan="1" rowspan="1">420</td><td colspan="1" rowspan="1">112.4</td><td colspan="1" rowspan="1">58.7</td><td colspan="1" rowspan="1">8.6</td><td colspan="1" rowspan="1">420.3</td></tr><tr><td colspan="1" rowspan="1">PDI</td><td colspan="1" rowspan="1">420</td><td colspan="1" rowspan="1">0.21</td><td colspan="1" rowspan="1">0.09</td><td colspan="1" rowspan="1">0.05</td><td colspan="1" rowspan="1">0.62</td></tr><tr><td colspan="1" rowspan="1">Zeta potential (mV)</td><td colspan="1" rowspan="1">420</td><td colspan="1" rowspan="1">-18.3</td><td colspan="1" rowspan="1">15.4</td><td colspan="1" rowspan="1">-67.2</td><td colspan="1" rowspan="1">34.5</td></tr><tr><td colspan="1" rowspan="1">Yield (%)</td><td colspan="1" rowspan="1">420</td><td colspan="1" rowspan="1">53.1</td><td colspan="1" rowspan="1">18.9</td><td colspan="1" rowspan="1">12.0</td><td colspan="1" rowspan="1">92.5</td></tr><tr><td colspan="1" rowspan="1">Cell viability (24 h, %)</td><td colspan="1" rowspan="1">420</td><td colspan="1" rowspan="1">77.6</td><td colspan="1" rowspan="1">18.3</td><td colspan="1" rowspan="1">22.0</td><td colspan="1" rowspan="1">99.8</td></tr><tr><td colspan="1" rowspan="1">MIC (µg/mL)</td><td colspan="1" rowspan="1">180</td><td colspan="1" rowspan="1">112.5</td><td colspan="1" rowspan="1">84.2</td><td colspan="1" rowspan="1">6.0</td><td colspan="1" rowspan="1">320.0</td></tr></tbody></table>Table 1. Descriptive statistics for experimental dataset (n = 420). MIC reported for metal-based NP subset (n = 180).Model predictive performance. Table 2 reports hold-out test performance for the three surrogate model classes across the principal continuous outputs (size, zeta potential, PDI, yield, cell viability). Performance is averaged over five repeats of nested cross-validation; standard deviations are reported to indicate model stability.<table style="min-width: 100px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><th colspan="1" rowspan="1">Model</th><th colspan="1" rowspan="1">Output</th><th colspan="1" rowspan="1">R2 (mean ± SD)</th><th colspan="1" rowspan="1">RMSE (mean ± SD)</th></tr><tr><td colspan="1" rowspan="1">Random Forest</td><td colspan="1" rowspan="1">Size (nm)</td><td colspan="1" rowspan="1">0.86 ± 0.03</td><td colspan="1" rowspan="1">9.8 ± 1.2</td></tr><tr><td colspan="1" rowspan="1">Random Forest</td><td colspan="1" rowspan="1">Zeta (mV)</td><td colspan="1" rowspan="1">0.79 ± 0.04</td><td colspan="1" rowspan="1">3.6 ± 0.5</td></tr><tr><td colspan="1" rowspan="1">GPR</td><td colspan="1" rowspan="1">Size (nm)</td><td colspan="1" rowspan="1">0.81 ± 0.05</td><td colspan="1" rowspan="1">11.4 ± 1.8</td></tr><tr><td colspan="1" rowspan="1">GPR</td><td colspan="1" rowspan="1">Zeta (mV)</td><td colspan="1" rowspan="1">0.83 ± 0.03</td><td colspan="1" rowspan="1">3.2 ± 0.4</td></tr><tr><td colspan="1" rowspan="1">ANN</td><td colspan="1" rowspan="1">Size (nm)</td><td colspan="1" rowspan="1">0.82 ± 0.06</td><td colspan="1" rowspan="1">10.9 ± 2.0</td></tr><tr><td colspan="1" rowspan="1">ANN</td><td colspan="1" rowspan="1">Zeta (mV)</td><td colspan="1" rowspan="1">0.77 ± 0.05</td><td colspan="1" rowspan="1">3.9 ± 0.9</td></tr><tr><td colspan="1" rowspan="1">Random Forest</td><td colspan="1" rowspan="1">Cell viability (%)</td><td colspan="1" rowspan="1">0.74 ± 0.06</td><td colspan="1" rowspan="1">8.2 ± 1.1</td></tr><tr><td colspan="1" rowspan="1">GPR</td><td colspan="1" rowspan="1">Cell viability (%)</td><td colspan="1" rowspan="1">0.70 ± 0.08</td><td colspan="1" rowspan="1">9.1 ± 1.4</td></tr></tbody></table>Table 2. Surrogate model performance on held-out test sets (mean ± SD over 5 repeats).These results show that Random Forests provided superior predictive accuracy for hydrodynamic diameter, while Gaussian Process Regression offered better calibrated uncertainty for zeta potential predictions. ANNs were competitive but exhibited higher variance across folds, consistent with the dataset size and heterogeneity (Shukla & Bandyopadhyay, 2023).Feature importance and interpretability. SHAP analysis applied to the Random Forest model identified precursor concentration, pH, capping agent concentration, and reaction temperature as the most influential features for particle size and PDI (Figure 1 shows a SHAP summary visualization). For zeta potential and antibacterial MIC, synthesis route (chemical vs. green) and reducing agent ratio had strong effects. Sensitivity analysis revealed non-linear interactions: for example, increasing capping agent concentration reduced size only at moderate pH ranges (6–8), while at high pH (>9) the effect plateaued, consistent with known colloidal stabilization mechanisms (Unsoy et al., 2012; Rauwel, 2017).<img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/ai-driven-optimization-of-nanoparticle-synthesis-parameters-for-tailored-biomedical-applications-iklba/figure-1-1778389650418.png" alt="SHAP summary plot showing feature contributions to predicted particle size and MIC" style="max-width: 100%; height: auto; object-fit: contain;">Figure 1. SHAP summary plot showing feature contributions to predicted particle size and MICOptimization outcomes. Table 3 compares baseline DOE/RSM recipes against AI-derived optimized recipes from BO and GA for the two target profiles. Improvements are reported relative to the baseline RSM recipe and averaged across three experimentally validated replicates for each recommended recipe.<table style="min-width: 150px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><th colspan="1" rowspan="1">Target Profile</th><th colspan="1" rowspan="1">Method</th><th colspan="1" rowspan="1">Mean Size (nm)</th><th colspan="1" rowspan="1">PDI</th><th colspan="1" rowspan="1">Cell viability (%) / MIC (µg/mL)</th><th colspan="1" rowspan="1">Yield (%)</th></tr><tr><td colspan="1" rowspan="1">Drug delivery</td><td colspan="1" rowspan="1">RSM baseline</td><td colspan="1" rowspan="1">128 ± 11</td><td colspan="1" rowspan="1">0.26 ± 0.03</td><td colspan="1" rowspan="1">72.3 ± 5.8 (viability)</td><td colspan="1" rowspan="1">49.6 ± 3.5</td></tr><tr><td colspan="1" rowspan="1">Drug delivery</td><td colspan="1" rowspan="1">Bayesian opt.</td><td colspan="1" rowspan="1">94 ± 6</td><td colspan="1" rowspan="1">0.15 ± 0.02</td><td colspan="1" rowspan="1">88.7 ± 4.1 (viability)</td><td colspan="1" rowspan="1">62.4 ± 4.2</td></tr><tr><td colspan="1" rowspan="1">Drug delivery</td><td colspan="1" rowspan="1">Genetic alg.</td><td colspan="1" rowspan="1">101 ± 7</td><td colspan="1" rowspan="1">0.17 ± 0.02</td><td colspan="1" rowspan="1">86.5 ± 3.9 (viability)</td><td colspan="1" rowspan="1">58.9 ± 3.8</td></tr><tr><td colspan="1" rowspan="1">Antibacterial (Ag)</td><td colspan="1" rowspan="1">RSM baseline</td><td colspan="1" rowspan="1">52 ± 4</td><td colspan="1" rowspan="1">0.22 ± 0.03</td><td colspan="1" rowspan="1">MIC 78 ± 8</td><td colspan="1" rowspan="1">45.3 ± 3.7</td></tr><tr><td colspan="1" rowspan="1">Antibacterial (Ag)</td><td colspan="1" rowspan="1">Bayesian opt.</td><td colspan="1" rowspan="1">24 ± 3</td><td colspan="1" rowspan="1">0.12 ± 0.02</td><td colspan="1" rowspan="1">MIC 21 ± 4</td><td colspan="1" rowspan="1">54.8 ± 4.1</td></tr><tr><td colspan="1" rowspan="1">Antibacterial (Ag)</td><td colspan="1" rowspan="1">Genetic alg.</td><td colspan="1" rowspan="1">28 ± 3</td><td colspan="1" rowspan="1">0.14 ± 0.02</td><td colspan="1" rowspan="1">MIC 26 ± 5</td><td colspan="1" rowspan="1">52.6 ± 3.9</td></tr></tbody></table>Table 3. Comparison of baseline RSM recipes with AI-derived optimized recipes (experimental validation, mean ± SD over n = 3 replicates per recipe).As shown in Table 3, Bayesian optimization yielded the most favorable trade-offs for both target profiles, producing drug-delivery nanoparticles with mean diameters < 100 nm, low PDI, and improved cell viability relative to RSM baselines (improvements of 18–23% across metrics). For antibacterial silver nanoparticles, BO achieved particle size reductions to <30 nm and lowered MIC values by ~73% compared with baseline. GA-derived solutions were close to BO but showed slightly larger variance in experimental validation, reflecting the heuristic nature of population search.<img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/ai-driven-optimization-of-nanoparticle-synthesis-parameters-for-tailored-biomedical-applications-iklba/figure-2-1778389655752.png" alt="Pareto front showing trade-off between particle size and cell viability for drug-delivery target, with BO and GA solutions plotted" style="max-width: 100%; height: auto; object-fit: contain;">Figure 2. Pareto front showing trade-off between particle size and cell viability for drug-delivery target, with BO and GA solutions plottedModel-to-bench transferability. Mean absolute error between predicted and experimentally measured size for validated BO recipes was 7.1 nm (≈7% relative error), demonstrating acceptable transfer from in silico optimization to bench synthesis. For MIC predictions, mean absolute percentage error was 18%, denoting larger uncertainty for biological endpoints but still useful for guiding experimental effort.Algorithmic efficiency. BO required approximately 180 surrogate evaluations to converge on stable Pareto sets for each target, whereas GA convergence typically required 200 generations with population evaluations equal to 200 per generation; in computational terms BO was more sample-efficient due to uncertainty-guided exploration (Syed, 2025).<h2>Discussion</h2>The present study demonstrates that AI-driven surrogate modeling combined with metaheuristic and Bayesian optimization can rapidly identify synthesis parameter sets that deliver tailored nanoparticle properties for distinct biomedical applications. The results align with prior domain studies that reported successful application of ML to nanoparticle formulation and green-synthesis optimization (Putra et al., 2025; Kapoor et al., 2024; Khan et al., 2026) but extend these findings by systematically comparing multiple surrogate model classes, optimization strategies, and by providing experimental validation of recommended recipes.Model performance and choice. Random Forest achieved the highest accuracy for predicting hydrodynamic diameter, consistent with the ensemble model's robustness to heterogeneous data and mixed variable types (Shukla & Bandyopadhyay, 2023). Gaussian Process Regression, while slightly less accurate for size, provided better-calibrated uncertainty estimates critical for Bayesian optimization and safe exploration of synthesis space (Syed, 2025). ANN models were competitive but required tighter hyperparameter tuning and larger data volumes to match RF and GPR stability, a pattern reported in other engineering applications (Sampath, 2025; Shukla & Bandyopadhyay, 2023).Optimization gains and trade-offs. Bayesian optimization consistently produced Pareto-optimal recipes that improved key metrics relative to RSM baselines. The greater sample-efficiency of BO is particularly valuable in nanoparticle synthesis where each experimental run can be time- and resource-intensive. GA produced comparable optima but typically required more surrogate evaluations and exhibited slightly higher experimental variance in validation, indicating that practitioners may prefer BO for constrained experimental contexts and GA when broad global exploration of rugged landscapes is required (Syed, 2025).Explainability and mechanistic insights. SHAP-based interpretability reinforced mechanistic expectations (e.g., higher capping agent concentrations and neutral-to-moderate pH favor smaller, monodisperse particles) while uncovering nuanced interactions (e.g., temperature–pH coupling) that can inform reaction mechanism hypotheses (Unsoy et al., 2012; Rauwel, 2017). These insights are valuable because they enable experimenters to judge whether AI-recommended recipes are chemically plausible before bench implementation, increasing trust and facilitating adoption (Kapoor et al., 2024).Biological endpoints and uncertainty. Predicting biological end-points (cell viability, MIC) remains more challenging than physicochemical metrics—larger model uncertainty and higher error rates were observed. This likely reflects additional sources of variability (biological assay conditions, batch effects, nanoparticle–bio interface complexity) and underscores the importance of incorporating assay-specific metadata and larger labeled datasets to improve predictive fidelity (Kapoor et al., 2024; Mandal & Bhattacharjee, 2024). Despite this, AI-derived recipes substantially improved biological performance relative to baselines, demonstrating practical utility even with present uncertainties.Limitations. Several limitations merit emphasis. First, dataset size and diversity constrain generalizability: although 420 experiments span multiple synthesis routes, additional data—especially for specific NP chemistries and biological assays—would enhance model robustness. Second, the study used surrogate-based optimization rather than closed-loop autonomous experimentation; integrating active learning and real-time experimental feedback would likely accelerate convergence and mitigate model error. Third, scale-up considerations (e.g., reaction vessel geometry, mixing regimes) were not explicitly modeled; recipes optimized at bench scale may require additional tuning for manufacturing contexts (Anand et al., 2022).Practical recommendations. Based on results, we recommend a staged adoption pathway for laboratories aiming to deploy AI-driven synthesis optimization: (1) assemble a curated experimental dataset with consistent metadata and measurement protocols; (2) train ensemble models (Random Forest) for rapid baseline predictions and GPR for uncertainty-aware BO loops; (3) use SHAP to screen AI-derived recipes for chemical plausibility; (4) validate top candidates experimentally with triplicate runs and iteratively update the surrogate models. This pragmatic workflow balances predictive accuracy, uncertainty management, and experimental safety and is consistent with recent proposals for AI-driven formulation pipelines (Kapoor et al., 2024; Sampath, 2025).Relation to prior work. The results complement prior application-specific optimization studies: Putra et al. (2025) demonstrated AI-driven parameter selection for heavy-metal remediation, and Khan et al. (2026) applied RSM for green synthesis antibacterial optimization. Our multi-algorithm comparison and experimental validation provide a broader methodological foundation and show that hybrid AI/metaheuristic frameworks can deliver cross-cutting benefits across biomedical targets.Future work. Future research should focus on (a) integrating active learning and closed-loop experimentation to enable autonomous optimization; (b) expanding datasets to include in vivo performance and pharmacokinetic endpoints for translational applications; (c) embedding process-scale variables to facilitate scale-up; and (d) exploring transfer learning across nanoparticle chemistries to leverage existing datasets and reduce new experimental burden (Kapoor et al., 2024).<h2>Conclusion</h2>This study presents an integrated AI-driven optimization pipeline that combines surrogate modeling (Random Forest, Gaussian Process Regression, ANN) with Bayesian and metaheuristic optimization to derive synthesis parameter recommendations for tailored biomedical nanoparticle applications. Across a dataset of 420 experiments, AI-derived recipes improved physicochemical and biological performance compared to classical RSM baselines, with Bayesian optimization showing particularly favorable sample-efficiency and experimental transferability. Explainability analyses (SHAP) provided mechanistic insights that increased the interpretability and trustworthiness of recommendations. Limitations include remaining uncertainty for biological endpoints and scale-up considerations. We conclude that AI-driven optimization is a powerful enabler for accelerating the rational design of nanoparticles for biomedical use and that coupling these methods with active learning and expanded datasets will further enhance utility and translational impact.Data availability: Curated data and code used for surrogate modeling and optimization are available on reasonable request to the corresponding author.<h2>References</h2><ol><li>Putra, A., Rahmawati, S., Fajar, M., Yuliana, D. (2025). 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