STIMULUS OF ARTIFICIAL NEURAL NETWORK IN DRUG DISCOVERY, DEVELOPMENT AND RESEARCH

Abstract

Artificial neural networks (ANNs) methods are representation of pattern gratitude proficiencies of the neural

networks of the human brain. Also, to a single neuron in the brain, artificial neuron unit receives signals from many

external sources, processes them, and makes pronouncements. Fascinatingly, ANN simulates the biological nervous

system and draws on analogues of adaptive biological neurons. Experimental designs for the ANNs methods are

laid-back and can map functions using historical or incomplete data, which makes them a powerful key for

simulation of several non-linear systems. ANNs in particular, have involved enormous attention due to the variety of

advantages they offer over the conventional methods. Among these advantages the ability to adapt, fast speed,

massive parallelism, and robustness are the most profound. Since of their capacity for making predictions, pattern

recognition, and modelling, ANNs have been very useful in many aspects of pharmaceutical research including

modelling of the brain neural network, analytical data analysis, drug modelling, protein structure and function,

dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modelling, and in vitro in vivo

correlations.