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THE PARADIGM OF ARTIFICIAL INTELLIGENCE (AI) IN DRUG DISCOVERY AND DRUG DEVELOPMENT - A REVIEW

Authors: Miss. Pemmaka Vara Lalitha, Mr. V. S. Chandrasekaran*, Dr. M. Kishore Babu;

THE PARADIGM OF ARTIFICIAL INTELLIGENCE (AI) IN DRUG DISCOVERY AND DRUG DEVELOPMENT - A REVIEW

Abstract

Artificial intelligence has decreased the workload of humans and improved the human standard of living. Artificial intelligence widening over the world not only in drug discovery and development of the drug but also in other aspects too. This article is mainly related to a paradigm shift of artificial intelligence (ai) in drug discovery and development to make them more operative and precise. Artificial intelligence has been increasing more in pharmaceutical industries. Conventional drug discovery is a spectacularly time-consuming and overpriced process. At the same time ai tools are modifying every stage of the drug discovery and development process, ai tools alter the speed and economics of the pharmaceutical industry. Artificial intelligence has been altering dramatically in many strands of pharmaceuticals. In this review article, we focus on the use of ai in different regions of pharmaceutical industries, along with drug discovery and development, enhancing pharmaceutical productivity and clinical trials. Keywords: Paradigm, Artificial Intelligence, Machine Learning, Deep Learning

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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