ARTIFICIAL INTELLIGENCE IN PHARMACY DRUG DESIGN

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Keywords:

Drug discovery, Artificial Intelligence systems, Computer-Aided Drug Discovery, Quantitative Structure-Activity Relationship (QSAR) approach.

Abstract

Drug discovery can be viewed as a challenging multi-dimensional issue  in which various properties of compounds including efficacy, pharmacokinetics, and safety need to be enhanced in respect to giving drug molecules. Contemporary progress in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as Artificial Intelligence systems that refine the design thesis through report investigation are now giving a cornerstone for the establishment of greater automation into detail of this process. Artificial intelligence (AI) has stimulated computer-aided drug discovery. This could likely speed up time duration for compound discovery and enhancement and authorize more productive hunts of related chemicals. But, such optimization also increases substantial theories, technical, and organizational queries, as well as suspicion about the ongoing boost around them. The worldwide acceptance of machine learning, in particular deep learning, in multiple scientific disciplines, and the development in computing hardware and software, among other factors, continue to power this development.

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Published

12-01-2022

How to Cite

KALAYIL, N., SHONA S. D’SOUZA, SHOWKHIYA Y. KHAN, and PALLAVI PAUL. “ARTIFICIAL INTELLIGENCE IN PHARMACY DRUG DESIGN”. Asian Journal of Pharmaceutical and Clinical Research, vol. 15, no. 3, Jan. 2022, https://innovareacademics.in/journals/index.php/ajpcr/article/view/43890.

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