Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo, China.
International Doctoral Innovation Centre, NingboTech University, Ningbo, China.
Comput Intell Neurosci. 2022 Aug 8;2022:8640115. doi: 10.1155/2022/8640115. eCollection 2022.
Artificial neural network (ANN) techniques are widely used to screen the data and predict the experimental result in pharmaceutical studies. In this study, a novel dissolution result prediction and screen system with a backpropagation network and regression methods was modeled. For this purpose, 21 groups of dissolution data were used to train and verify the ANN model. Based on the design of input data, the related data were still available to train the ANN model when the formulation composition was changed. Two regression methods, the effective data regression method (EDRM) and the reference line regression method (RLRM), make this system predict dissolution results with a high accuracy rate but use less database than the orthogonal experiment. Based on the decision tree, a data screen function is also realized in this system. This ANN model provides a novel drug prediction system with a decrease in time and cost and also easily facilitates the design of new formulation.
人工神经网络（ANN）技术广泛用于筛选数据和预测药物研究中的实验结果。本研究建立了一个具有反向传播网络和回归方法的新型溶出度结果预测和筛选系统。为此，使用 21 组溶出度数据对 ANN 模型进行训练和验证。基于输入数据的设计，当配方组成发生变化时，仍可利用相关数据来训练 ANN 模型。两种回归方法，即有效数据回归法（EDRM）和参考线回归法（RLRM），使该系统能够以较高的准确率预测溶出度结果，且比正交实验使用的数据库更少。基于决策树，该系统还实现了数据筛选功能。该 ANN 模型为药物预测系统提供了一种新颖的方法，可减少时间和成本，并且易于设计新的配方。
