Artificial Neural Network in Drug Delivery and Pharmaceutical Research
Vijaykumar Sutariyaa, *, Anastasia Grosheva, Prabodh Sadanab, Deepak Bhatiab, Yashwant Pathaka
Identifiers and Pagination:Year: 2013
Issue: Suppl-1, M5
First Page: 49
Last Page: 62
Publisher Id: TOBIOIJ-7-49
Article History:Received Date: 06/08/2013
Revision Received Date: 06/09/2013
Acceptance Date: 15/09/2013
Electronic publication date: 13/12/2013
Collection year: 2013
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.