ARTIFICIAL INTELLIGENCE IN PHARMACY DRUG DESIGN

NISHA V KALAYIL; SHONA S D’SOUZA; SHOWKHIYA Y KHAN; PALLAVI PAUL

doi:10.22159/ajpcr.2022.v15i4.43890

Authors

NISHA V KALAYIL Department of Pharmaceutics, St. John College of Pharmacy and Research, Palghar, Maharashtra, India. https://orcid.org/0000-0002-4327-0428
SHONA S D’SOUZA Department of Pharmaceutics, St. John College of Pharmacy and Research, Palghar, Maharashtra, India. https://orcid.org/0000-0003-1715-2599
SHOWKHIYA Y KHAN Department of Pharmaceutics, St. John College of Pharmacy and Research, Palghar, Maharashtra, India.
PALLAVI PAUL Department of Pharmaceutics, St. John College of Pharmacy and Research, Palghar, Maharashtra, India. https://orcid.org/0000-0003-1297-7823

DOI:

https://doi.org/10.22159/ajpcr.2022.v15i4.43890

Keywords:

Drug discovery, Artificial intelligence systems, Computer-aided drug discovery, Quantitative structure-activity relationship approach

Abstract

Drug discovery is said to be a multi-dimensional issue in which different properties of drug candidates including efficacy, pharmacokinetics, and safety need to be improved with respect to giving the final drug product. Current advances in fields such as artificial intelligence (AI) systems that refine the design thesis through report investigation, microfluidics-assisted chemical synthesis, and biological testing are now giving a cornerstone for the establishment of greater automation into detail of this process. 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. However, such optimization also increases substantial theories, technical, and organizational queries, as well as suspicion about the ongoing boost around them. 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 worldwide.

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