ARTIFICIAL NEURAL NETWORKS: FUNCTIONINGANDAPPLICATIONS IN PHARMACEUTICAL INDUSTRY
| Autor: | M Ankith, Damodharan N, S P Surya Teja |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Computer science business.industry Process (engineering) media_common.quotation_subject Pharmaceutical Science 02 engineering and technology 021001 nanoscience & nanotechnology Machine learning computer.software_genre 030226 pharmacology & pharmacy Thinking processes Backpropagation 03 medical and health sciences 0302 clinical medicine Pattern recognition (psychology) Artificial intelligence Predictability 0210 nano-technology Function (engineering) business Pharmacology Toxicology and Pharmaceutics (miscellaneous) computer media_common Pharmaceutical industry |
| Zdroj: | International Journal of Applied Pharmaceutics. 10:28 |
| ISSN: | 0975-7058 |
| Popis: | Artificial Neural Network (ANN) technology is a group of computer designed algorithms for simulating neurological processing to process information and produce outcomes like the thinking process of humans in learning, decision making and solving problems. The uniqueness of ANN is its ability to deliver desirable results even with the help of incomplete or historical data results without a need for structured experimental design by modeling and pattern recognition. It imbibes data through repetition with suitable learning models, similarly to humans, without actual programming. It leverages its ability by processing elements connected with the user given inputs which transfers as a function and provides as output. Moreover, the present output by ANN is a combinational effect of data collected from previous inputs and the current responsiveness of the system. Technically, ANN is associated with highly monitored network along with a back propagation learning standard. Due to its exceptional predictability, the current uses of ANN can be applied to many more disciplines in the area of science which requires multivariate data analysis. In the pharmaceutical process, this flexible tool is used to simulate various non-linear relationships. It also finds its application in the enhancement of pre-formulation parameters for predicting physicochemical properties of drug substances. It also finds its applications in pharmaceutical research, medicinal chemistry, QSAR study, pharmaceutical instrumental engineering. Its multi-objective concurrent optimization is adopted in the drug discovery process, protein structure, rational data analysis also. |
| Databáze: | OpenAIRE |
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