A pharmaceutical formulation is composed of system based on a computational techniques, several formulation factors and process variables. Optimization techniques are used in industrial planning, allocation, scheduling, decision-making, etc. An artificial neural network is a computational simulation of a biological neural network. These models mimic the real life behaviour of neurons and the electrical messages they produce between input, processing by the brain and the final output from the brain. There are many types of neural networks designed and new ones are invented but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN structure consists of one input layer, one or more hidden layers and one output layer. This feature of ANN, to extract information from the data presented to them, proves them to be powerful and flexible tools for modelling and predictive purposes and offers great potential for applications in a various field. ANNs are being used in pharmaceutical research to predict the nonlinear relationship between causal factors and response variables. Over past decades ANN have gained more acceptance in pharmaceutical formulation modelling because of some advantages like enhance product quality and performance at low cost and need short time to market and new product development, improve response of product as well as customer and improve confidence. The review enlists various dosage form developed, utilizing ANN and various ANN softwares used for this purpose.
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