|عنوان فارسی مقاله:||شبکه های عصبی مصنوعی در تحقیقات مرتبط با تحویل دارو و موضوعات دارویی|
|عنوان انگلیسی مقاله:||Artificial Neural Network in Drug Delivery and Pharmaceutical Research|
|رشته های مرتبط:||مهندسی کامپیوتر، هوش مصنوعی و مهندسی الگوریتم ها و محاسبات|
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2. مدل سازی شبکه های عصبی مصنوعی
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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.
In the past decade, neural networks have received a great deal of attention among scientists and engineers and they are being touted as one of the greatest computational tools ever developed. Much of this excitement is due to the ability of neural networks to emulate the brain’s ability to learn by example. This network makes decision and draws conclusionseven when presented with incomplete information. Moreover, at some primitive level, neural network imitates brain’s creative process in adapting to a novel situation . It is a very good statistical tool for many numeric as well as nonnumeric calculations. Specifically, ANNs are known to be a powerful tool to simulate various non-linear systems and have been applied to numerous problems of considerable complexity in many fields, including engineering , psychology, medicinal chemistry [2, 3], diagnostics [4, 5], and pharmaceutical research .
2. ARTIFICIAL NEURAL NETWORKS MODELING As biologically inspired computational model, ANN is capable of simulating neurological processing ability of the human brain. Average human brain contains about 100 billions of neurons with each neuron being connected with 1000-10,000 connections to others. A single neuron consists of three major parts—dendrites (fine branched out threads) carrying signals into the cell, the cell body receiving and processing the information, and the axon (a single longer extension) (Fig. 1). The axon carries the signal away and relays it to the dendrites of the next neuron or receptor of a target cell. The signals are conducted in all-or-none fashion through the cells. All the connections in the brain enable it to learn, recognize patterns, and predict outcomes.Similarly to the brain, ANN is composed of numerous processing units (PE), artificial neurons. The connections among all the units vary in strength, which is defined by coefficients or weights. The ANN mimics working of human brain and potentially fulfills the cherished dream of scientists to develop machines that can think like human beings.ANNs simulate learning and generalization behavior of the human brain through data modeling and pattern recognition for complex multidimensional problems. A significant difference between an ANN model and a statistical model is that the ANN can generalize the relationship between independent and dependent variables without a specific mathematical function. Thus, an ANN works well for solving nonlinear problems of multivariate and multiresponse systems such as space analysis in quantitative structure-activity relationships in pharmacokinetic studies  and structure prediction in drug development . There are many types of neural networks with new ones being continually invented; however, all ANNscan be characterized by their transfer functions of their processing units (PE), the learning rules, and by the connections formulas. PE, building component of ANN, receives many signals as weighted process variables from the response of other units . The most commonly applied ANN layout is forward propagating network trained by error backpropagation developed by Rumehart et al. . The forward propagation network consists of input layer, one or more hidden layers and one output layer (Fig. 2) . The input layer provides data from the external source. The mapping of the input data occurs by neural network hidden layers, then the final representative signal is generated by the output layer [10, 12]. The ability of neural networks to classify information depends on hidden layers, which are fully connected by the synapses to the neighboring layers.