‘Applications of machine learning in liposomal formulation and development’
Sina M. Matalqah, Zainab Lafi, Qasim Mhaidat, Nisreen Asha, Sara Yousef Asha
Abstract
Machine learning (ML) has emerged as a transformative tool in drug delivery, particularly in the design and optimization of liposomal formulations. This review focuses on the intersection of ML and liposomal technology, highlighting how advanced algorithms are accelerating formulation processes, predicting key parameters, and enabling personalized therapies. ML-driven approaches are restructuring formulation development by optimizing liposome size, stability, and encapsulation efficiency while refining drug release profiles. Additionally, the integration of ML enhances therapeutic outcomes by enabling precision-targeted delivery and minimizing side effects. This review presents current breakthroughs, challenges, and future opportunities in applying ML to liposomal systems, aiming to improve therapeutic efficacy and patient outcomes in various disease treatments.