Artificial Neural Network Process Used in Global Drug Discovery and Development

Abstract

One of the branches of Artificial Intelligence (AI) that is most frequently employed in the advancement of medicine is artificial neural networks (ANN). An alternative name for neural networks is ANN. These are the computing systems inclined by the biological nervous system. It is built on a network of interconnected components known as artificial neurons. Similar to a biological neuron, the ANN is also having the interconnection nodes. The ANN is a simple mathematical function. This model has three sets of rules. Those are multiplication, summation and activation. In order to predict the properties of chemical compounds for drug discovery, ANNs are used. It has been demonstrated mathematically that an ANN may be used to roughly predict the link between any chemical property and its structure. To understand the significance of ANN in the drug discovery and development process is the primary goal of this article. Information was acquired from the USFDA’s official website as well as other research and review journals. ANN have been widely applied in a variety of key pharmacy-related fields, from clinical pharmacy to bio pharmacy to the interpretation of analytical data and the creation of drugs and dosage forms. It is commonly implemented in many areas of the pharmaceutical industry, including quantitative structure-activity relationships (QSAR) research and the analysis of chemicals used in drug discovery and development. ANN is useful for resolving nonlinear problems in multivariate and multi-response systems, including space analysis in QSAR in pharmacokinetic studies and structure prediction in drug development.