A Generative Adversarial Network Approach to Predict Nanoparticle Size in Microfluidics.

To achieve precise control over the properties and performance of nanoparticles (NPs) in a microfluidic setting, a profound understanding of the influential parameters governing the NP size is crucial. This study specifically delves into poly(lactic--glycolic acid) (PLGA)-based NPs synthesized through microfluidics that have been extensively explored as drug delivery systems (DDS). A comprehensive database, containing more than 11 hundred data points, is curated through an extensive literature review, identifying potential effective features. Initially, we employed a tabular generative adversarial network (TGAN) to enhance data sets, increasing the reliability of the obtained results and elevating prediction accuracy. Subsequently, NP size prediction was performed using different machine learning (ML) techniques including decision tree (DT), random forest (RF), deep neural networks (DNN), linear regression (LR), support vector regression (SVR), and gradient boosting (GB). Among these ensembles, DT emerges as the most accurate algorithm, yielding an average prediction error of 8%. Further simulations underscore the pivotal role of the synthesis method, poly(vinyl alcohol) (PVA) concentration, and lactide-to-glycolide (LA/GA) ratio of PLGA copolymers as the primary determinants influencing NP size.