Development of generalized artificial neural network model for prediction of the performance of modified release tablets

Abstract

Artificial Intelligence is the simulation of human intelligence. From delivering simple groceries to door steps to solving the toughest task in scientists lab, it is surrounding human life in all the means. So how can the Pharma industry be untouched in the case of AI?! Artificial Neural Network (ANN) is a type of AI used to solve non-linear problems and predict the output for given input parameters from the training values. In this research work, such generalized ANN is developed to predict drug release from the sustained-release tablet. It is trained by the back propagation method under supervised learning. For training purposes various data has been collected from practical work as well as some openly available patents. An IFS (Input Feature Selection) was applied with a leave one out approach to attain a suitable dataset. Various learning variables like learning rate, momentum coefficient have been studied with various levels to achieve optimum model. This developed model is evaluated on the basis of RMSE, similarity and dissimilarity factors and can predict the output with the best achieved average error ~0.0095 and R2 0.9953. Such ANNs can be the best combination of experience and intelligence, which can eliminate tedious lab work that can be cost-effective and time-effective. newline