在药物制剂的开发过程中,需要一个强大的工具来从复杂的工艺参数和材料属性中提取关键点。人工神经网络(ANN),一种有前途且更灵活的建模技术,可以以生物神经网络的方式以高度并行性和分布式模式解决实际复杂的问题。基于人工神经网络的数据挖掘和分析具有替代数百个试错实验的能力。自1990年代以来,人工神经网络已被药剂学研究人员用于数据分析,现在已成为药物科学的研究方法。本文综述了人工神经网络在预测中的最新应用进展,药物配方的表征和优化,为药剂学和人工神经网络的进一步跨学科研究提供参考。
During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This
review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.