Machine learning-guided high throughput nanoparticle design

Abstract

Designing nanoparticles with desired properties is a challenging endeavor, due to the large combinatorial space and complex structure-function relationships. High throughput methodologies and machine learning approaches are attractive and emergent...

Leveraging machine learning to streamline the development of liposomal drug delivery systems

“…Further, microfluidic production offers fine control over critical process parameters (CPPs) such as flow rates, pressure, and temperature, which are crucial for optimizing liposome size, lamellarity, encapsulation efficiency, and stability. 21–23 Despite these benefits, however, several challenges hinder clinical and industrial applications. The primary obstacle is translating bench-scale liposomal production to larger scales, as traditional methods often do not scale up seamlessly.…”

“…In recent years, the application of machine learning (ML) and artificial intelligence (AI) in pharmaceutical sciences has gained traction due to their ability to model highdimensional problems. 21,22,[28][29][30][31] ML is valuable throughout the entire drug discovery and development pipeline, 21,32 from exploring chemical space to identify new hit compounds [32][33][34][35][36] , to predicting synthetic pathways, [37][38][39] Absorption-Distribution-Metabolism-Excretion Toxicity (ADMET) properties 40,41 and target activity. 42 However, many methods rely on external datasets often lacking quality.…”