Author(s) :

Dr. Gajanan A. Vaishnav, Abhay Shripad Joshi.

Volume/Issue :

Abstract :

Artificial neural networks (ANNs) are a type of machine learning model that are inspired by the structure and function of the human brain. In pharmaceutical analysis, ANNs can be used for a variety of tasks, including the identification of active ingredients in a drug and the prediction of side effects. Artificial neural networks (ANNs) have been applied to a many pharmaceutical issues, including design of drugs, identification of drug targets, and toxicity prediction. The most common type of ANNs are feedforward neural networks, where the output of one layer is not connected to the input of the same layer again. Artificial neural networks (ANNs) can achieve high levels of accuracy and precision in tasks such as drug discovery and drug design, as well as in the analysis of large and complex datasets. They can also be used to analyze large-scale genomic and proteomic data, which can aid in the discovery of new drug targets and biomarkers. Artificial neural networks (ANN) have been applied to pharmaceutical analysis with a great deal of success. By using ANNs in pharmaceutical analysis, we can improve accuracy and reliability while reducing the overall cost of drug development. Artificial neural networks have become an invaluable tool for advanced data analytics in the field of pharmacy.

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