Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Abstract

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

Digital Pharmaceutical Sciences

“…ANNs are particularly powerful in modeling nonlinear relationships and can make highly accurate predictions due to their ability to analyze complex data primarily based on generalization and pattern recognition (39,40). Nevertheless, some challenges with using ANNs can be encountered, such as trapping at local minima, controlling noise, and overfitting/ underfitting.…”

Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics

“…The information is then summed and transferred to the hidden layer using certain activation functions which ultimately send the results to the output layer 8 . ANNs are powerful machine learning tools for modelling nonlinear relationships which are frequently encountered in diverse research areas and industrial settings 9 , 10 .…”

Application of machine learning in prediction of hydrotrope-enhanced solubilisation of indomethacin

“…Such selection, however, might be much more conveniently made in silico using artificial neural networks (ANNs). ANNs are biologically inspired computational models capable of simulating the brain's ability to learn by example; they provide particularly powerful tools to aid in the modeling of non-linear relationships and have found numerous applications in the pharmaceutical sciences (Gawehn et al 2016, Marsland 2015, Sutariya et al 2013). …”