Abstract:
Recent studies have focused on leveraging artificial neural networks (ANNs) to
optimize the formulation of pharmaceutical drug micro-emulsions, specif-ically
targeting the ideal composition of surfactants, co-surfactants, oil, wa-ter, and
process factors that determine key characteristics like stability, drop-let size,
and clarity of liquid dosage forms [1]. ANNs, a machine learning technique
inspired by biological neural networks in animal brains, are capa-ble of
identifying patterns and correlations within complex datasets, allowing them to
forecast or optimize systems effectively. By training ANNs to rec-ognize these
relationships, they can be used to enhance the formulation pro-cess, making
predictions that guide optimization efforts [2]
